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SelectArena: A Modern Benchmark for Feature Selection on Real-world Tabular Data
Bastian Schäfer, Dominika Matus, Lennart Purucker, Ricardo Knauer, Erik Rodner, Frank Hutter, Anton Sokolov, Arnaud Liefooghe
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Summary
The paper introduces SelectArena, a modern benchmark for feature selection on real-world tabular data. It addresses gaps in existing benchmarks by evaluating 15 widely used feature selection methods across 71 manually curated real-world datasets using state-of-the-art tabular foundation models. The study assesses methods based on predictive performance, runtime, stability, and validity, revealing that F-Test is a balanced choice, while tree-based methods (CART, RFImportance) are Pareto-optimal for performance and runtime. Search-based methods perform poorly across most metrics.
Entities (10)
Relation Signals (10)
SelectArena → evaluates → 15 feature selection methods
confidence 95% · We compare 15 widely used feature selection methods on 71 manually curated real-world tabular datasets
SelectArena → uses → 71 manually curated real-world tabular datasets
confidence 95% · We compare 15 widely used feature selection methods on 71 manually curated real-world tabular datasets
SelectArena → builton → TabArena
confidence 90% · and TabArena (Erickson et al., 2025)
SelectArena → builton → AutoGluon
confidence 90% · Our method implementation builds on AutoGluon
F-Test → isbalancedchoice → across metrics
confidence 90% · F-Test emerging as a balanced choice across our metrics.
RFImportance → isparetooptimalfor → predictive performance and runtime
confidence 90% · Tree-based methods, CART and RFImportance... are Pareto-optimal when taking into account predictive performance and runtime.
CART → isparetooptimalfor → predictive performance and runtime
confidence 90% · Tree-based methods, CART and RFImportance... are Pareto-optimal when taking into account predictive performance and runtime.
SelectArena → usesdownstreammodels → TabICLv2
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Abstract
Abstract:Selecting a subset of features (columns) from a tabular dataset is a ubiquitous challenge in real-world applications. Industry experts seek to minimize model training costs, climate scientists require an interpretable subset of factors to understand extreme weather events, and clinicians need to choose among diagnostic tests due to financial and time constraints. When looking at the scientific literature to determine which feature selection method to use, practitioners across domains face an unrepresentative landscape of benchmarking. Despite extensive research, existing feature selection benchmarks provide limited guidance because they rely on outdated models, ad hoc evaluation protocols, or artificially generated data that do not reflect real-world problems. To close the gap between scientific benchmarking and real-world applications, we introduce SelectArena, a modern benchmark for feature selection. We compare 15 widely used feature selection methods on 71 manually curated real-world tabular datasets, covering classification and regression problems, using standardized evaluation protocols and state-of-the-art tabular (foundation) models. Our results reveal clear trade-offs across predictive performance, runtime, stability, and validity. Tree-based methods, CART and RFImportance, along with F-Test are Pareto-optimal when taking into account predictive performance and runtime. F-Test, information-theory-, and distance-based methods lead on stability and validity, with F-Test emerging as a balanced choice across our metrics. Search-based methods are slow, unstable and achieve low performance. SelectArena turns these findings into practical guidance and, as an extensible platform, supports future feature selection research.
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- Source: https://arxiv.org/abs/2610.13644v1
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SELECTARENA: A MODERN BENCHMARK FOR FEA- TURE SELECTION ON REAL-WORLD TABULAR DATA Bastian Schäfer 1,∗ Dominika Matus 2,∗ Lennart Purucker 3,2 Ricardo Knauer 4 Erik Rodner 4 Frank Hutter 3,5,2 Anton Sokolov 1 Arnaud Liefooghe 1 1 Université du Littoral-Côte-d’Opale 2 University of Freiburg 3 Prior Labs 4 HTW Berlin 5 ELLIS Institute Tübingen ∗ Equal contribution. bastian.schafer@univ-littoral.fr, matusd@cs.uni-freiburg.de ABSTRACT Selecting a subset of features (columns) from a tabular dataset is a ubiquitous chal- lenge in real-world applications. Industry experts seek to minimize model training costs, climate scientists require an interpretable subset of factors to understand extreme weather events, and clinicians need to choose among diagnostic tests due to financial and time constraints. When looking at the scientific literature to deter- mine which feature selection method to use, practitioners across domains face an unrepresentative landscape of benchmarking. Despite extensive research, existing feature selection benchmarks provide limited guidance because they rely on out- dated models, ad hoc evaluation protocols, or artificially generated data that do not reflect real-world problems. To close the gap between scientific benchmarking and real-world applications, we introduce SelectArena, a modern benchmark for feature selection. We compare 15 widely used feature selection methods on 71 manually curated real-world tabular datasets, covering classification and regression problems, using standardized evaluation protocols and state-of-the-art tabular (foundation) models. Our results reveal clear trade-offs across predictive performance, runtime, stability, and validity. Tree-based methods, CART and RFImportance, along with F-Test are Pareto-optimal when taking into account predictive performance and runtime. F-Test, information-theory-, and distance-based methods lead on stability and validity, with F-Test emerging as a balanced choice across our metrics. Search- based methods are slow, unstable and achieve low performance. SelectArena turns these findings into practical guidance and, as an extensible platform, supports future feature selection research. LaplacianScore Random LOCO SBE SFS GainRatio mRMR (R)ReliefF MarkovBlanket Lasso F-Test MI ElasticNet CART RFImportance 900 1000 1100 1200 1300 Elo 0.0 0.2 0.4 0.6 0.8 1.0 unstable stable 0.0 0.2 0.4 0.6 0.8 1.0 invalid valid Figure 1: SelectArena at a glance. We present Elo ratings of the15feature selection methods ordered left to right from worst- to best-performing method. Elo rating summarizes how often a method beats another on the same dataset, budget, and downstream model, with random selection anchored at1000 (Appendix D.2). Error bars are95%confidence intervals from1000cluster bootstrap resamples over the71datasets. The left bar of each pair is shaded by stability (how much the selected subset changes across resamples) and the right bar by validity (how well injected noise features are rejected), where darker is better for both. 1 arXiv:2610.13644v1 [cs.LG] 9 Oct 2026 1INTRODUCTION Tabular data dominates many medical, industrial, and scientific applications (Borisov et al., 2022; Erickson et al., 2025). However, collecting such data is often costly and requires labor-intensive measurements, expensive instruments, or access to specialized facilities (Hédou et al., 2024; Wang et al., 2025). At the same time, the growing dimensionality of datasets decreases interpretability, increases model training and inference costs, and forces practitioners to trade off performance against computation time. Feature selection addresses these challenges by identifying a subset of the most informative features, thereby reducing data collection and training costs while improving transparency and interpretability. Early work (Guyon & Elisseeff, 2003) has framed the objectives of feature selection around predictive performance, computational cost, and interpretability. Later work has added stability (Nogueira & Brown, 2016) and validity (Guyon et al., 2007a) as standard criteria for reliable feature selection. Within this problem formulation, hundreds of methods have been developed (Li et al., 2018), each making different trade-offs across these metrics. The benchmarking literature, however, remains highly fragmented and despite its long history (Jain & Zongker, 1997; Kohavi & John, 1997; Reunanen, 2003), does not provide sufficient guidance to practitioners. Existing benchmarks have not kept pace with the development of modern tabular models, in particular foundation models, and rely on unrealistic evaluation protocols. Most use small-scale comparisons on synthetic or limited real-world datasets with≤10 methods and≤20 real-world datasets (Bommert et al., 2020; Cherepanova et al., 2023; Bakhmach et al., 2026) (Section 2, Table 1). To address these gaps, we introduce SelectArena, a benchmark of 15 feature selection methods evaluated on 71 manually curated real-world tabular datasets across predictive performance, runtime, stability, and validity, using state-of-the-art tabular (foundation) models. Our contributions can be summarized as follows: •We analyze 368 datasets from the feature selection and machine learning literature and manually curate a set of 71 real-world tabular datasets following a strict data curation protocol. We release the resulting datasets. •We review 296 feature selection methods and select a set of 15 methods according to a strict method curation protocol. Where well-maintained codebases are unavailable, we provide our own implementations. •We provide a standardized evaluation pipeline covering predictive performance, stability, and validity, alongside runtime, and evaluate all methods with five downstream models including state-of-the-art tabular foundation model. We share our code to make our pipeline open-source. Our results reveal clear trade-offs across predictive performance, runtime, stability, and validity. Tree-based (RFImportance, CART) and regularization-based methods (ElasticNet, Lasso) achieve the highest predictive performance, while F-Test and GainRatio are the fastest and, alongside MI and LaplacianScore, lead on stability and validity. With all four criteria taken into account, F-Test emerges as the most balanced choice across them. Search-based methods are the counterexample: they are slow and rank near the bottom on performance and stability. However, no method wins on every criterion, so the right choice still depends on which objective a practitioner prioritizes. SelectArena turns these findings into practical guidance and, as an extensible open-source platform, supports future feature selection research. 2RELATED WORK Recent years have seen numerous feature selection benchmarks. Despite extensive research, practi- tioners still lack clear guidance on selecting the most suitable method for a given context. Existing benchmarks are typically limited in scope by examining only a small number of methods, most often no more than 10 (Bouchlaghem et al., 2022; Kohavi & John, 1997), or by focusing exclusively on specific subgroups (Bommert et al., 2020; Hopf & Reifenrath, 2021). Moreover, many studies rely on unrepresentative or limited data sources, such as synthetic data (Bolón-Canedo et al., 2013; Hopf & Reifenrath, 2021), domain-specific data (Bakhmach et al., 2026; Li et al., 2022; Umar et al., 2025), 2 or non-tabular data (text or image) (Guyon et al., 2007a; Kou et al., 2020; Miao & Niu, 2016). In most cases, the number of real-world tabular datasets does not exceed 20. Furthermore, most benchmarks focus only on predictive performance (Chandrashekar & Sahin, 2014; Reunanen, 2003; Sheikhpour et al., 2017). Few studies consider the stability of the selected subsets (Bommert et al., 2020; Brown et al., 2012; Kou et al., 2020; Saeys et al., 2008), even fewer the validity (Bolón-Canedo et al., 2013; Cherepanova et al., 2023; Guyon et al., 2007a) or both (Bakhmach et al., 2026; Hopf & Reifenrath, 2021). In addition, even recent works predominantly rely on classical downstream models, with little consideration of state-of-the-art approaches like tuned gradient-boosted trees or pretrained tabular foundation models (Bakhmach et al., 2026; Cherepanova et al., 2023; Umar et al., 2025). In contrast, SelectArena comprehensively benchmarks a diverse set of feature selection methods across a large and broad dataset collection, evaluates the predictive performance as well as the stability and validity, and incorporates state-of-the-art models. Table 1 provides a systematic comparison of existing feature selection benchmarks and SelectArena with respect to included methods, datasets, models, and evaluation metrics. Table 1: Comparison of feature selection benchmarks. We present former benchmarks in compari- son to SelectArena. # Methods: how many out of the total number of benchmark methods correspond to our first three curation criteria (not a variant of a method and applicable across tabular classification and regression tasks). # Datasets: how many out of the total number of benchmark datasets satisfy our curation criteria for high-quality tabular data (Section 3.2). Domain: visualizes the dataset domain:(synthetic),(environmental),(biomedical),(technological),(scientific), (financial) or(not applicable). SotA models: whether the benchmark includes state-of-the-art downstream models, such as tuned gradient-boosted trees or pretrained tabular foundation models (Erickson et al., 2025), with the options(yes) or(no). The final three columns (Perf, Stab, Val) indicate whether the benchmark reports an evaluation of the predictive performance, stability, or validity. Benchmark # Meth- ods # Data- sets Domain SotA models PerfStabVal Kohavi & John (1997)3/45/14 Jain & Zongker (1997)8/150/1 Reunanen (2003)2/22/7 Guyon et al. (2007a)1/30/5 Saeys et al. (2008)4/46/6 Brown et al. (2012)7/94/15 Bolón-Canedo et al. (2013)7/110/11 Chandrashekar & Sahin (2014)3/42/6 Miao & Niu (2016)8/81/12 Sheikhpour et al. (2017)3/90/3 Li et al. (2018)18/457/25 Kou et al. (2020)1/100/10 Bommert et al. (2020)12/221/16 Hopf & Reifenrath (2021)12/580/13 Bouchlaghem et al. (2022)5/50/7 Li et al. (2022)7/80/15 Cherepanova et al. (2023)8/91/12 Schütz et al. (2025)8/101/11 Umar et al. (2025)1/10/3 Bakhmach et al. (2026)14/220/1 SelectArena (Ours)15/1571/71 3 3SELECTARENA 3.1PROBLEM SETUP In practical applications, feature acquisition is often constrained by limited resources such as time, financial cost, or computational overhead. As a result, only a subset of all available features can be collected or processed, making it essential to select the most informative features under a given budgetb. This setting can be formalized as follows: LetF = 1,...,pdenote the full feature set and letS ⊆ Fbe a selected subset with associated costc(S) = P i∈S c i . We define a model- dependent subset utility functionu M : 2 F → R, which evaluates the utility of a feature subsetS under a given modelM. The budget-constrained feature selection problem can then be formulated as:max S⊆F u M (S)subject toc(S)≤ b. In certain cases, this problem can be solved to optimality, for example via exhaustive enumeration when the feature (sub)set size is small or via best subset selection in some models (Tillmann et al., 2024). In practice, however, exact computation is often infeasible (Amaldi & Kann, 1998), and approximate (heuristic) solution methods are therefore more commonly employed. As a special case, when feature costs are uniform (c i = 1) and the budget is fixed to the cardinality (b = k), the constraint reduces to|S|≤ k, recovering the classical cardinality- constrained feature selection problem (Knauer & Rodner, 2023). While the budget-constrained formulation is more general, it may introduce a degree of arbitrariness in practice, as small changes in the budget can significantly alterSwithout necessarily reflecting meaningful differences in resource availability. By contrast, cardinality constraints provide a simpler, more natural abstraction in many settings and are therefore mostly adopted in practice and used in SelectArena. 3.2BENCHMARKING SETUP Method Curation Protocol.We reviewed 33 prominent survey, benchmark, and related papers, covering seminal works and specialized contributions across the feature selection landscape, from foundational theory to domain-specific applications and recent benchmarking with full list of works in Table B.1. From this pool, we identified 296 unique feature selection methods (Table B.2) and selected a representative set of methods based on the following criteria: (1) suitability for tabular data, (2) suitability for all tasks (binary classification, multi-class classification and regression), (3) methodological independence (not merely a variant of another method), (4) number of citations in the relevant literature, and (5) subgroup coverage. The 15 feature selection methods that have been included in SelectArena reflect a mix of widely adopted and methodologically diverse approaches, with Table 2 and Figure 3 summarizing their key characteristics. For methods requiring model coupling, we use LGBM. More details on the curation criteria, curation insights, as well as an overview of all investigated methods, their source, the number of citations in the relevant literature, and the final decision on inclusion is available in Appendix B. Our method implementation builds on AutoGluon (Erickson et al., 2020) and TabArena (Erickson et al., 2025). We introduce a feature selection routine into TabArena that is integrated in the model- agnostic preprocessing pipeline of AutoGluon. The routine enforces a cardinality constraintk (Section 3.1), a time limit and a fallback to random selection in case of timeout (Appendix D.2) and custom preprocessing per method (Appendix B.4). To support time-bounded execution, the remaining runtime is periodically checked and intermediate results are returned when the limit is reached. Dataset Curation Protocol.Real-world tabular datasets are the foundation of SelectArena, so we build on the curation standards of TabArena (Erickson et al., 2025) and extend its dataset collection towards feature selection. In order to do so, we surveyed the feature selection survey and benchmark literature, the papers of our selected methods, and the related explainable AI literature, covering 73 articles in total (Appendix C.1), from which we identified 368 candidate datasets. We then applied the following curation criteria: (1) unique predictive task definition, (2) tabular data (e.g., no text or image data), (3) minimum of 10 features for meaningful selection, (4) real-world origin (no synthetic data), (5) IID data suitable for random splitting, (6) genuine predictive task, (7) no ethical concerns with the predictive task, and (8) accessible source data. Implementing these criteria results in 71 high-quality real-world datasets in a curation process presented in Figure 2. The datasets span many domains (Table 1), cover diverse prediction tasks and differ in size as well as feature dimensionality (Figure 3). More details on the curation criteria, curation insights, and on overview of the selected datasets with their key properties can be found in Appendix C. 4 Table 2: Benchmarked feature selection methods. We report all included methods in SelectArena. Determinism: whether a method yields the same subset for the same data across repeated evaluations (also referred to as consistency in Dembinsky et al. (2026)). The options are(yes),(no), or (model-dependent). Model coupling: whether feature selection can be performed in conjunction with the downstream modelM(related to portability in Dembinsky et al. (2026)). Subgroup: we specify which subgroup a method belongs to (Appendix A). Citations: reflects a method’s popularity and impact within the relevant feature selection literature, measured as the frequency of occurrence across relevant survey and benchmark works (Section 3.2). For implementation details, please refer to Appendix B.4. NameDeter-ModelSubgroupCita- Implemen- minism couplingtitonstation CART (Breiman et al., 1984)Tree4scikit-learn ElasticNet (Zou & Hastie, 2005) Regularization3scikit-learn F-test (Ståhle et al., 1989) Correlation6scikit-learn GainRatio (Quinlan, 1986)Info theory3Us LaplacianScore (He et al., 2005)Distance5Us Lasso (Tibshirani, 1996)Regularization8scikit-learn LOCO (Duch, 2006) Leave-one-out3Us MarkovBlanket (Koller & Sahami, 1996)Backward search3Us mRMR (Peng et al., 2005)Info theory12Us Mutual Information (Battiti, 1994)Info theory6scikit-learn Random (Baseline) //Us (R)ReliefF (Kononenko et al., 1997)Distance10Us RFImportance (Breiman, 2001)Tree8scikit-learn SBE (Guyon et al., 2002)Backward search8Us SFS (Cover & Van Campenhout, 1977) Forward search10Us 3 6 834 024 013116114 020512871 - 28 No unique task - 10 0 No tabular data - 3 5 Not at least 10 features - 4 4 No real- world origin - 21 Non- IID data - 9 No predictive task - 3 Ethical concern - 57 Not accessible Figure 2: Dataset curation process. The reason and number of datasets excluded in each step of our dataset curation pipeline are shown, along with the remaining number of datasets after each step. Downstream Models.To evaluate the selected subset of features on the subsequent prediction tasks, we use five downstream models: linear model (Linear), random forest (RF) (Breiman, 2001), LightGBM (LGBM) (Ke et al., 2017), RealMLP (Holzmüller et al., 2025) and TabICLv2 (Qu et al., 2026) spanning diverse inductive biases. We use AutoGluon’s default configuration in our experiments. For LGBM, we conduct an ablation study on the impact of hyperparameter tuning (Appendix D.2). Hardware usage.CPU experiments (stability, validity and performance with the Linear, RF and LGBM model) ran on an AMD EPYC 9655 with 96 cores and GPU experiments (performance with RealMLP and TabICLv2) primarily on an NVIDIA L40 with 48 GB VRAM and in rare cases on NVIDIA H200 141 GB VRAM. Our performance experiments span 15 methods, 71 datasets, five budgets, five downstream models, and three seeds, yielding 79,875 runs, totaling approximately 292 GPU days and 685 CPU days. Validity with 3 noise levels and stability results with 25 bootstrapped repetitions and respectively 15 methods, 71 datasets and 5 repetitions took additional 2243 CPU days. 5 1980s1990s2000s2010s2020s 0 2 4 6 8 10 12 14 16 18 Number of Datasets Dataset Publication Decade Binary Multiclass Regression 0 10 20 30 40 Number of Datasets Datasets by Problem Type 1980s1990s2000s 0 2 4 6 Number of Methods Method Publication Decade YesNo 0 2 4 6 8 10 Number of Methods Model Coupling 10 2 10 3 10 4 10 5 Number of Samples 10 1 10 2 10 3 10 4 Number of Features Classification Regression 5 10 15 20 Number of Classes Figure 3: Method and dataset characteristics. SelectArena represents a diverse collection of widely adopted feature selection methods as well as high-quality real-world datasets. 3.3EVALUATION We assess the quality of a feature selection method in four dimensions: predictive performance, stability, validity, and runtime. For cardinality-constrained feature selection, we need to consider two hyperparameters that are independent of the specific method: the cardinality constraintkand the time constraint. To obtain a diverse performance estimate across different application scenarios, we varyk as a function of the number of featuresp. For datasets with≤ 100features, we varyk =⌈p× j/6⌉, wherepis the number of features andj ∈1,..., 5is an index variable. For bigger datasets, we use an exponential regime:k = ⌈p j/6 ⌉. For more details, see Appendix C.5. If methods do not respect the cardinality constraint, we use random fallback (see Appendix D.2). With respect to the time constraint, we use a limit of one hour per feature selection experiment consisting of a (dataset, budget, model)-tuple for all feature selection methods. The downstream model training and inference time is set to one hour per experiment as well. Predictive Performance.The predictive performance of a feature selection method is the per- formance of the downstream model using the selected feature subset on a given task. It is mea- sured by ROC AUC for binary classification problems, log-loss for multi-class classification, and RMSE for regression. We discuss the choice of evaluation metrics in Appendix D.2. To com- pare across these metrics, we report Elo ratings (Appendix D.2) and improvement over RANDOM, I(m,d) = 100· Err(r,d)− Err(m,d) /Err(r,d), whereErr(m,d)is the metric error of method mon datasetdandrdenotes random selection (Appendix D.2). We repeat all experiments for three seeds. Stability.For interpretability, feature selection methods should not only achieve strong predictive performance, but also yield similar subsets for similar data, for instance via resampling of the original data. We refer to this desideratum as stability, which is sometimes also described as robustness, reliability, or continuity (Dembinsky et al., 2026). We assess the stability with the Nogueira metric (Nogueira & Brown, 2016) across 25 bootstrap samples. The Nogueira estimator corrects for chance, asymptotically assigns random selections a score of 0, and reaches a maximum of 1. Values below 0.40 indicate poor stability, values between 0.40 and 0.75 reflect intermediate to good stability, and values above 0.75 denote excellent stability (Nogueira & Brown, 2016). Validity.In addition to being stable, feature selection methods should also recover relevant rather than irrelevant features, an interpretability desideratum that we refer to as validity. In the literature, this property is also described as truthfulness, faithfulness, or fidelity (Dembinsky et al., 2026). However, assessing validity is challenging in the absence of a synthetic ground truth (Bakhmach et al., 2026; Bolón-Canedo et al., 2013; Hopf & Reifenrath, 2021). We follow prior work (Cherepanova et al., 2023; Guyon et al., 2003; 2004; 2007a) and evaluate validity by augmenting the original feature set with uninformative Gaussian noise variables at 50%, 75%, and 100% of the original feature count. 6 We then operationalize validity by quantifying the proportion of selected features from the original feature set, which we term selection precision. This measure ranges from 0 to 1. In our setting, the selection precision is equivalent to the selection recall, i.e., the proportion of original, informative features selected, when the recall is normalized by its maximum possible value for k. Runtime.Lastly, feature selection methods should produce well-performing, stable, and valid subsets of features as fast as possible. Therefore, we measure the wall-clock time a method needs to produce a feature subset for a given budgetkwith the upper bound of one hour. Where the implementation allows it, exceeding the limit interrupts the method and triggers a random fallback that restores the subset to sizek, either by adding randomly chosen unselected features or, for backward methods, by randomly subsampling the current set. To make runtimes comparable across datasets of different dimensionality, we normalize by the number of features and report seconds per 1,000 features. 4RESULTS We evaluated 15 widely used feature selection methods on 71 manually curated real-world tabular datasets with five cardinality constraints per dataset and five downstream predictors across three seeds in our performance experiments. The results are shown in Figures 1 and 4 and discussed further in the next paragraphs. For stability and validity runs, we use the same 15 methods and 71 datasets respectively and 5 repetitions. Each stability run consists of 25 bootstrapped repetition, each validity run includes 3 noise levels. The results are shown in Figures 5 and 6, respectively, and discussed in the last paragraphs. Strong Methods Can Be Fast.The left plot in Figure 4 shows improvement over Random (%) versus selection time (seconds per 1k features), with predictive performance and runtime on the axes; exact scores are in Table D.2. The Pareto front consists of RFImportance and CART, the tree-based methods, together with the correlation-based F-Test. All three rank in the top four on predictive performance alone (MI ranks second) and in the six fastest on runtime alone (with LaplacianScore, GainRatio, and MarkovBlanket), so practitioners who value both face little trade-off. The search- based methods (LOCO, SBE, and SFS) sit at the opposite corner, performing poorly while being the slowest, although this, at least in part, can be attributed to the imposed time limit. 10 0 10 1 10 2 10 3 10 4 Selection Time (s) per 1k Features 10 12 14 16 18 20 22 24 26 28 Impr. over Random (%) optimal (R)ReliefF CART ElasticNet F-Test GainRatio LOCO Lasso MI MarkovBlanket SBE RFImportance SFS mRMR Dominated Methods Pareto Front RFImportance MI CART F-Test mRMR MarkovBlanket ElasticNet Lasso GainRatio (R)ReliefF SFS LOCO SBE LaplacianScore TabICLv2 RF RealMLP LGBM Linear 29.025.525.924.223.623.521.720.320.821.719.314.613.2-19.6 27.725.824.823.821.123.622.121.220.620.317.512.713.0-17.0 27.024.224.923.121.621.820.919.220.320.618.714.812.5-18.4 25.522.923.521.521.721.221.621.119.818.518.113.012.3-23.2 24.623.222.022.921.819.022.621.718.416.317.712.08.4-14.4 01020 Impr. over Random (%) Figure 4: Predictive performance. Both panels report improvement over RANDOM (%, higher is better), averaged over three seeds, five cardinalities, and 71 datasets. (Left): Improvement averaged over downstream models against feature selection time (s) per 1,000 features with Pareto front marked in orange. We exclude LaplacianScore as it performs worse than Random. (Right): Improvement for every model–method interaction. Good Feature Selection Helps Every Model.The heatmap in Figure 4 presents improvement over Random (%) for each combination of feature selection method and downstream model averaged over seeds, budgets, and datasets. We observe that model ordering is rather consistent across methods, with TabICLv2 ranking first for eight of the 14 methods and Linear last for seven. The improvement spread across models within a method (at most 5.4p) is far smaller than the spread across methods within a model (15.8p under TabICLv2). This asymmetry suggests that even the most expressive model, like TabICLv2, cannot recover predictive signal from a poorly selected subset and so the choice of feature selection method is crucial. We find no evidence that aligning the inductive biases 7 0.00.23.415.1217.42799.7 Events per Variable F-Test LaplacianScore MI GainRatio (R)ReliefF mRMR RFImportance ElasticNet MarkovBlanket CART Lasso SFS SBE LOCO Random 0.0 0.2 0.4 0.6 0.8 1.0 unstable stable Figure 5: Stability. Nogueira score (Nogueira & Brown, 2016) across datasets. We use the events per variable, i.e., the number of samples per feature for regression problems or the number of samples in the minority class per feature for classification problems, as a proxy for the selection difficulty (Harrell, 2015; Heinze et al., 2018). of selector and predictor helps. Although Lasso and ElasticNet peak with the Linear model (but also only by 0.5p), the tree-based RFImportance and CART achieve best performance with TabICLv2 rather than with RF or LGBM. Strong Methods are not Necessarily Stable.The stability of the feature selection methods is shown in Figure 5. Most methods are stable when the number of samples (in the minority class) per feature is sufficiently large, but become increasingly unstable in low-data regimes (Harrell, 2015; Heinze et al., 2018). F-Test and distance-based methods exhibit the highest stability contributing to a better interpretability of the selected features, followed by information-theoretic methods. F-Test and MI are the only high-performing methods that rank high in stability. Due to its high fallback rates, search-methods are only slightly more stable than a random selection. An extended stability analysis can be found in Appendix D.3. 0.00.10.76.340.02799.7 Events per Variable F-Test GainRatio MI LaplacianScore ElasticNet Lasso (R)ReliefF SFS RFImportance mRMR LOCO SBE CART Random MarkovBlanket 0.0 0.2 0.4 0.6 0.8 1.0 invalid valid Figure 6: Validity. Selection precision across datasets at a noise level of 50%. Other noise levels can be found in Appendix D. Like for the stability, we use the events per variable, i.e., the number of samples per feature for regression problems or the number of samples in the minority class per feature for classification problems, as a proxy for the selection difficulty (Harrell, 2015; Heinze et al., 2018). Strong Methods do not Necessarily Select Informative Features.Figure 6 shows the validity of the feature selection methods. In contrast to the stability, the validity is less sensitive to the number 8 of samples (in the minority class) per feature. F-Test and information-theoretic methods achieve the highest validity aiding interpretability of the selection, followed by distance-based methods. F-Test and MI are the only high-performing methods that rank high in validity. MarkovBlanket exhibits lower validity than even a random selection, potentially because it favors removing redundant features that are prevalent in real-world data (Guyon & Elisseeff, 2003; Koller & Sahami, 1996). An extended validity analysis at different noise levels can be found in Appendix D.4. F-Test Is Strong on All Four Axes.Taken together, no single method dominates on all four evaluation axes, but among the strong candidates F-Test comes closest to being a well-balanced choice. It lies on the Pareto front of predictive performance and runtime, and ranks highest on both stability and validity (Figure 1). RFImportance and CART achieve higher Elo ratings but are less stable, and the model-coupled search methods are competitive on none of the four dimensions under our one-hour budget. 5CONCLUSIONS Feature selection is essential across domains where practitioners must balance predictive performance, computational constraints, and interpretability. However, existing benchmarks offer limited guidance to select methods that best suit different real-world requirements. To address this gap, we introduce SelectArena, a large-scale benchmark for feature selection on 71 manually curated real-world tabular datasets, spanning classification and regression tasks. We evaluate 15 widely used feature selection methods using standardized protocols and five downstream models, including a tabular foundation model. SelectArena is not only the largest general feature selection benchmark to date, but also the only one to include leading tabular models. Limitations.While SelectArena substantially advances the state of feature selection benchmarking, several limitations remain. (1) All feature selection methods are evaluated under a one-hour time limit, which results in slower methods not always completing their task (up to 47% of runs for SBE). Since we handle timeouts with a random fallback that keeps the feature budget fixed, the results for the affected methods reflect their performance under the limit on our hardware and are subject to change if given more compute. Appendix D.2 provides an ablation without the random fallback. (2) Several methods are implemented by us in cases where no sufficiently maintained alternative was available, rather than taken from an established library (Table 2). Although we follow the original algorithms, our implementations may be less optimized than library equivalents, which can disadvantage them under a wall-clock limit. (3) Our analysis of stability and validity relies on bootstrapping and Gaussian noise, respectively. Both are arguably simpler than what methods face in practice, since bootstrapping resamples preserve the original feature correlations and Gaussian noise variables are independent of both the target and the informative features. Other forms of perturbation may affect feature selection behavior differently, e.g., non-Gaussian noise, outliers, missing values, distribution shifts, or class imbalances. (4) All methods are constrained to return exactlykfeatures, however, some of them natively determine the subset size intrinsically, such as Lasso and ElasticNet via the regularization path or MarkovBlanket via conditional independence tests. Imposing a fixed cardinality makes them comparable but does not evaluate them as originally intended, and may understate their performance. (5) The observed performance of feature selection methods strongly depends on the evaluation metrics used. Using the ROC AUC for binary classification, log-loss for multiclass classification, and RMSE for regression (Appendix D.2), we employ widely adopted standard metrics, but alternative criteria such as calibration or fairness may lead to different results. Recommendations.Our results lead to several practical recommendations: (1) When predictive performance is the primary objective, practitioners might want to choose a tree-based method such as RFImportance or CART. These methods are also strong candidates when runtime is considered. (2) For interpretability-focused applications, F-Test emerges as the best choice. It ranks highest in terms of stability and validity, next to information-theory and distance-based methods, with a competitive predictive performance and runtime. (3) The choice of a suitable downstream model for a feature selection method can be important. A state-of-the-art model like TabICLv2 is mostly, but not always, a better predictor than RF, LGBM or even a linear model. Therefore, practitioners should evaluate whether simpler downstream models may be preferable to more complex and computationally expensive alternatives. 9 REPRODUCIBILITY STATEMENT SelectArena is an open-source framework. We publish the code and our curated datasets. More information can be found in Appendix D.1. In the Benchmarking Setup (Chapter 3.2), we detail the hardware setup used for the experiments. With fully providing soft- and hardware details as well as the datasets used, SelectArena is fully reproducible. ACKNOWLEDGMENT STATEMENT We thank the dataset donors for sharing their data, especially those under a public license. Further- more, we thank the community, particularly OpenML, Kaggle, and Hugging Face, for developing tools that facilitate the sharing of tabular data. We thank the author of the scikit feature library for the inspiration through the provided implementations of feature selection methods. B. Schäfer, A Liefooghe and A. Sokolov have benefited from the support of the National Research Agency under France 2030, MAIA Project ANR-22-EXES-0009, and from the "Pôle Métropolitain Côte d’Opale" (PMCO). D. Matus, L. Purucker, and F. Hutter acknowledge funding by the Deutsche Forschungs- gemeinschaft (DFG, German Research Foundation) under SFB 1597 (SmallData), grant number 499552394 R. Knauer and E. Rodner acknowledge being supported by the European Union’s Eras- mus+ program through EUonAIR, reference 101177370, and the Deutsche Forschungsgemeinschaft (DFG) through Berlin Initiative for Applied Foundation Model Research, reference 528483508. AUTHOR CONTRIBUTIONS B. Schäfer co-led the code development, led the method curation, co-led the dataset curation, contributed the method implementation, co-led the experiments, co-led the evaluation and the visualization and co-led the writing of the paper. D. Matus co-led the code development, contributed to the method curation, co-led the dataset curation, led the method implementation, co-led the experiments, co-led the evaluation and the visualization and co-led the writing of the paper. L. Purucker co-led the code development, supervised the method curation, supervised the dataset curation, supervised the experiments and supervised the writing of the paper. R. Knauer supervised the project and contributed to writing and reviewing the paper. E. Rodner supervised R. Knauer and helped with reviewing and editing the paper. F. Hutter supervised L. Purucker and D. Matus. A. Sokolov supervised B. Schäfer. A. Liefooghe supervised B. Schäfer and helped with reviewing and editing the paper. 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A Feature Selection Taxonomy for Practitioners23 B Methods23 B.1 Reviewed Surveys and Benchmarks . . . . . . . . . . . . . . . . . . . . . . . . .23 B.2 Method Curation Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23 B.3 Method Curation Insights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24 B.4 Method Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25 B.5 Overview of All Investigated Feature Selection Methods . . . . . . . . . . . . . .28 C Datasets33 C.1 Dataset Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .33 C.2 Dataset Curation Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .34 C.3 Dataset Curation Insights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .35 C.4 Overview of Selected Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . .35 C.5 Cardinality Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .38 D Benchmarking Details and Extended Results38 D.1 Open-source Benchmarking . . . . . . . . . . . . . . . . . . . . . . . . . . . . .38 D.2 Predictive Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .38 D.3 Stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .44 D.4 Validity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .46 D.5 Raw Predictive Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . .50 22 AFEATURE SELECTION TAXONOMY FOR PRACTITIONERS Feature selection methods are traditionally categorized as filters, wrappers, or embedded methods, depending on whether feature relevance is assessed independently of the downstream modelM, through repeated model evaluation, or during model training (Guyon & Elisseeff, 2003). This taxonomy can be useful from an implementation perspective, for example when deciding whether cardinality-constrained linear models should be realized via exhaustive enumeration (wrapper), mixed- integer programming (embedded method), or post-hoc training on a reduced subset (filter). However, it is less informative for practitioners, who are less concerned with where feature selection occurs in a pipeline (a structural perspective) and more with how the method approaches this combinatorial optimization problem (a functional perspective). We therefore adopt a complementary categorization into search and non-search methods (Kohavi & John, 1997). We define search methods as procedures that evaluate multiple subsets using a model-dependent subset utilityu M (S). Due to the exponential size of the search space, search methods differ primarily in how they explore it efficiently, for example through sequential forward or backward search. In contrast, non-search methods avoid repeated subset evaluations underu M (S) and instead rely on proxy mechanisms, such as univariate relevance or recursive partitioning. While non-search methods may still involve search procedures, they optimize a different objective than the subset utility in Section 3.1. They include correlation-based, distance-based, information-theoretic, and tree-based approaches (Duch, 2006), as well as regularization-based methods (Tibshirani, 1996; Zou & Hastie, 2005). Figure A.1 provides an overview of the categorization for SelectArena. While not exhaustive, the subgrouping can be readily extended to cover additional search methods, such as evolutionary algorithms (Eiben & Smith, 2015; Wang et al., 2025; Xue et al., 2015) and reinforcement learning (Mazyavkina et al., 2021; Sutton & Barto, 2018; Wang et al., 2025), as well as non-search methods, such as game-theoretic (Covert et al., 2021; Fryer et al., 2021; Wallsberger et al., 2024) and knowledge-driven approaches (Jeong et al., 2025; Knauer et al., 2025; Li et al., 2025). PERSPECTIVEM ETHODS STRUCTURAL FUNCTIONAL Where is the method implemented in the pipeline? How is the combinatorial feature selection problem tackled? Searc h met hodsNon- searc h met hods Forw ardBackw ardCorrelationDistance Information theory TreeRegularization FiltersWrappers Embedded methods Figure A.1: Taxonomy of feature selection methods for SelectArena. We complement the traditional structural perspective (Guyon & Elisseeff, 2003) with a functional categorization into search and non-search approaches (Kohavi & John, 1997). Search subgroups include forward and backward procedures, non-search subgroups are based on Duch (2006) and extended to include regularization-based methods (Tibshirani, 1996; Zou & Hastie, 2005). BMETHODS B.1REVIEWED SURVEYS AND BENCHMARKS Table B.1 lists the 33 works, predominantly surveys and benchmarks, that we reviewed to assess method popularity. For each feature selection method, the Citations column in Table 2 (Section 3.2) reports the number of these works in which the method is mentioned. We use this count to exclude methods with less than three mentions, in accordance with method curation criterion (4) (Section 3.2). B.2METHOD CURATION CRITERIA (1) Suitability for tabular data:Since SelectArena focuses on tabular datasets, we exclude methods that are primarily designed for other data modalities. For example, Information Gain for Text (Lee & Lee, 2006) is specific to textual data and therefore not included. (2) Suitability for all tasks (binary classification, multi-class classification and regression): 23 Table B.1: Reviewed survey and benchmark works. The 33 works used to determine the popularity of each feature selection method, ordered by year of publication. Jain & Zongker (1997)Bolón-Canedo et al. (2013)Li et al. (2018) Kohavi & John (1997)Wang et al. (2013)Bommert et al. (2020) Guyon & Elisseeff (2003)Chandrashekar & Sahin (2014) Kou et al. (2020) Reunanen (2003)Khalid et al. (2014)Covert et al. (2021) Duch (2006)Tang et al. (2014)Hopf & Reifenrath (2021) Guyon et al. (2007a)Jovi ́ c et al. (2015)Bouchlaghem et al. (2022) Saeys et al. (2007)Xue et al. (2015)Li et al. (2022) Zhao & Liu (2007)Miao & Niu (2016)Siegismund et al. (2022) Saeys et al. (2008)Li et al. (2017)Cherepanova et al. (2023) Meinshausen & Bühlmann (2010) Sheikhpour et al. (2017)Schütz et al. (2025) Brown et al. (2012)Heinze et al. (2018)Bakhmach et al. (2026) SelectArena includes datasets for binary classification, multi-class classification, and regression. Consequently, selected methods must support all target types. Methods restricted to a subset of tasks are therefore excluded. For instance,χ 2 (Liu & Setiono, 1995) is only applicable to classification problems and is thus not considered. (3) Methodological independence (not merely a variant of another method):The benchmark aims to include conceptually distinct feature selection methods rather than multiple variants of the same underlying approach. Therefore, methods such as Relief (Kira & Rendell, 1992) and ReliefF (Kononenko et al., 1997) are treated as methodologically similar, since ReliefF constitutes an extension of Relief. (4) Number of citations in the relevant literature:To ensure that the selected methods of the first cohort are established and widely recognized, we require each method to be cited at least three times across prominent surveys and benchmarking studies (Section 3.2). (5) Subgroup coverage:To promote methodological diversity, we employ a functional categorization of feature selection methods (Appendix A). The final method set in SelectArena is curated to ensure representation across methodological subgroups. B.3METHOD CURATION INSIGHTS Below, we share general insights we obtained during method curation: •Feature selection methods are frequently applied beyond the problem settings for which they were originally designed. For example, methods such asχ 2 or Gini Impurity are inherently intended for categorical targets, yet they are often applied to regression tasks, e.g., by discretizing the target variable. Such adaptations may substantially influence method behavior and evaluation outcomes. To avoid introducing methodological distortions, we exclude these methods from SelectArena and aim to raise awareness of this issue within the field. • A similar pattern can be observed with respect to data modalities. Many methods are used across heterogeneous data types, including tabular, textual, and image data, despite relying on assumptions tailored to a specific modality. Applying methods outside their intended data setting may lead to misleading conclusions and should therefore be considered carefully in empirical evaluations. • The feature selection literature is currently fragmented into several largely independent research streams, including classical statistical approaches, evolutionary approaches, and explainable AI (XAI) approaches. Despite using similar underlying techniques, these communities often evolve separately and make limited use of each other’s methodological advances. We believe that stronger integration across these research directions could accelerate progress within the field. 24 B.4METHOD IMPLEMENTATION As specified in Section 3.2, feature selection is implemented based on AutoGluon (Erickson et al., 2020) and TabArena (Erickson et al., 2025). We introduce a class called AbstractFeatureSelector, which is a specialization of AbstractFeatureGenerator and part of the preprocessing pipeline in AutoGluon. Before feature selection, AutoGluon’s default preprocessing infers feature types and removes constant, ID-like, and duplicate features. Categorical features keep the pandascategory dtype but are ordinally encoded, with categories seen fewer than two times in training treated as missing. There are two different types of AbstractFeatureSelectors: (1) the feature scoring type and (2) the subset selection type. Each feature selection method inherits the properties of the AbstractFeatureSelector, which are mainly the cardinality and time constraints, as well as a fallback in case the feature selection method does not return valid results. The fallback is implemented as a random feature selection using the intermediate results of the failed or canceled method, either sampling a score for the non-scored feature or completing the subset selection in case the intermediate subset is smaller than the cardinality constraint. The implementation is in most cases based on scikit-learn (Pedregosa et al., 2011) or scikit-feature (Li et al., 2018) (see Table 2 for details). The applied changes are usually the incorporation of a regular check for the time limit and a solution for returning the intermediate results when the time limit is met, as well as the introduction of pandas DataFrames instead of numpy arrays for clean data usage in the TabArena pipeline. In the following, we provide implementation details for all selected methods, including the assump- tions of the algorithms, the preprocessing, the type, a description of the approach, the criterion, and some additional information on the implementation: B.4.1CLASSIFICATION AND REGRESSION TREES (CART) (BREIMAN ET AL., 1984) Assumptions:Numerical features (sklearn). Preprocessing:Imputation. Type:Scoring. Approach:Greedy over tree structures (best split). Criterion:Mean decrease in impurity (MDI) per feature: total impurity reduction attributed to splits using that feature, where impurity is Gini for classification and variance for regression. Implementation:sklearn’sDecisionTreeRegressorand DecisionTreeClassifier. B.4.2ELASTICNET (ZOU & HASTIE, 2005) Assumptions:Numerical features (sklearn). Preprocessing:Imputation, scaling. Type:Scoring. Approach:Multivariate, embedded. Criterion:Magnitude of the coefficient| ˆ β j |from a joint linear (or logistic) fit with combined L1 and L2 regularization. Implementation:Weusesklearn’sElasticNetCVforregressionand LogisticRegressionCVwith elastic-net penalty for classification.We use the default L1-ratio grid but exclude the value 1.0 so the model never becomes pure Lasso. B.4.3F-TEST (STÅHLE ET AL., 1989) Assumptions:Numerical features (sklearn). Preprocessing:Imputation. Type:Scoring. Approach:Univariate. Criterion:F-statistic — sklearn’sf_classiffor classification (ratio of between- to within-class variance) andf_regressionfor regression (derived from squared Pearson correlation; equivalent in ranking to|r|). Implementation:We select the top-k features based on the returned F-statistics. 25 B.4.4INFORMATION GAIN RATIO (GAINRATIO) (QUINLAN, 1986) Assumptions:Discrete features (this implementation). Preprocessing:Imputation, discretization. Type:Scoring. Approach:Univariate. Criterion:Information gain ratioIGR = IG(Y,X) SI(X) = MI(Y,X) H(X) , whereXis a feature. The denominator penalizes high-cardinality features, which would otherwise dominate plain MI rankings. Implementation:We reuse sklearn’smutual_info_classiffor the numeratorIG(Y,X) and our own entropy estimator for the denominator H(X). B.4.5LAPLACIAN SCORE (HE ET AL., 2005) Assumptions:Numerical features. Preprocessing:Imputation, scaling (for the graph construction). Type:Scoring. Approach:Unsupervised. Criterion:Locality preservation (lower is better): a feature scores well if points that are close in the kNN graph have similar values for that feature. Implementation:We build a symmetric kNN graph with a Gaussian heat-kernel on Euclidean distances and compute the score via the Rayleigh quotient against the graph Laplacian, following the original paper. The scikit-feature version is quite outdated, so we re-implemented it. The hyperparameters k = 5 and t = median(d 2 ) are arbitrary choices. B.4.6LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR (LASSO) (TIBSHIRANI, 1996) Assumptions:Numerical features (sklearn). Preprocessing:Imputation, scaling. Type:Scoring. Approach:Multivariate, embedded. Criterion:Magnitude of the coefficient| ˆ β j |from a joint linear (or logistic) fit with L1 regularization. Implementation:We use sklearn’sLassoCVfor regression andLogisticRegressionCV with L1 penalty for classification. The CV variants pick the regularization strength internally, so no α/C tuning is needed. B.4.7LEAVE-ONE-COVARIATE-OUT (LOCO) (LEI ET AL., 2017) Assumptions:Proxy model assumptions. Preprocessing:None. Type:Scoring. Approach:Leave-one-out. Criterion:Per-feature performance drop from omission:LOCO j = L( ˆ f −j )−L( ˆ f full ), where ˆ f full is the proxy trained on all features and ˆ f −j is the proxy trained without feature j. Implementation:We use LGBM as the proxy (handles NaNs natively, no preprocessing required) and refit it once per feature for each split. Costly on high-dimensional data. B.4.8MARKOV BLANKET (KOLLER & SAHAMI, 1996) Assumptions:Numerical features. Preprocessing:Imputation. Type:Selection. Approach:Greedy multivariate (backward). Criterion:A featureX i is redundant if there existsM i ⊆ F \X i (its Markov blanket) such 26 thatY ⊥ X i | M i . In practice we score each feature by the cross-entropy/MSE difference under 5-fold CV between models fit onM i andM i ∪X i , and remove the feature whose absence hurts performance the least. Implementation:The blanketM i is taken to be the top-kPearson-correlated features ofX i . Models are fit and evaluated under 5-fold CV using Ridge for regression and Logistic Regression for classification. The original formulation (Koller & Sahami, 1996) searches for a blanket that renders Yconditionally independent of all remaining features; we restrict to a fixed top-kneighborhood. We use k = 5 and deviate from the original formulation by adding the regression case. B.4.9MINIMUM REDUNDANCY MAXIMUM RELEVANCE (MRMR) (PENG ET AL., 2005) Assumptions:Discrete features (this implementation). Preprocessing:Imputation, discretization. Type:Selection. Approach:Greedy multivariate (forward). Criterion:MID variant: at steptwith selected setS t−1 ,score(X i ) = I(X i ;Y ) − 1 |S t−1 | P X j ∈S t−1 I(X i ;X j ); the first feature is picked by max relevance I(X i ;Y ). Implementation:At each step we add the candidate maximizing relevance minus average pairwise redundancy with the already selected features. This is a re-implementation following scikit-feature’s MRMR. B.4.10MUTUAL INFORMATION (MI) (BATTITI, 1994) Assumptions:Numerical features (sklearn). Preprocessing:Imputation. Type:Scoring. Approach:Univariate. Criterion:Mutual information I(X,Y ) = H(X) + H(Y )− H(X,Y ). Implementation:We use sklearn’smutual_info_classiffor classification and mutual_info_regressionfor regression. Both use frequency counts for features marked as discrete and the Kraskov kNN estimator for continuous features. B.4.11RANDOM Assumptions:None. Preprocessing:None. Type:Selection. Approach:Random. Criterion:None. Implementation:Random selection of k features based on the random seed. B.4.12RANDOM FOREST IMPORTANCE (RFIMPORTANCE) (BREIMAN, 2001) Assumptions:Numerical features (sklearn). Preprocessing:Imputation. Type:Scoring. Approach:Multivariate, embedded. Criterion:Mean decrease in impurity (MDI) per feature, averaged over all trees in the forest: Gini impurity reduction for classification, variance reduction for regression. Implementation:Weusesklearn’sRandomForestRegressorand RandomForestClassifier with default hyperparameters. B.4.13SEQUENTIAL BACKWARD ELIMINATION (SBE) (GUYON ET AL., 2002) 27 Assumptions:Proxy model assumptions. Preprocessing:None. Type:Selection. Approach:Greedy multivariate (backward). Criterion:Holdout performance of the proxy model. Implementation:We use LGBM as the proxy model. At each step, the feature yielding smallest drop in performance after being excluded from the current subset is eliminated. B.4.14(R)RELIEFF (KONONENKO ET AL., 1997; ROBNIK-ŠIKONJA & KONONENKO, 1997) Assumptions:None. Preprocessing:Imputation, min-max scaling. Type:Scoring. Approach:Multivariate. Criterion:A feature is rewarded when it differs more on an instance’sknearest misses (different class) than on itsknearest hits (same class), with misses weighted by the class prior. RReliefF replaces the class machinery withP (diff feature| diff prediction), computed via Bayes rule running sums over kNN pairs. Implementation:Re-implemented to match the original algorithms, with a branched per-feature difference function:|a− b|for numeric features (on[0, 1]-scaled values) and1a̸= bfor nominal features. Summed across features, this gives the Manhattan-style distance used to build the kNN graph itself. For ReliefF, kNN structures are cached per class for efficiency, with small-class guards that divide by the actual number of neighbors returned (rather than byk) when a class has fewer thank + 1samples. Neighbor weighting is uniform (the paper also allows rank-based exponential weights). RReliefF is used for regression. We use k = 5 rather than the original k = 1. B.4.15SEQUENTIAL FORWARD SELECTION (SFS) (COVER & VAN CAMPENHOUT, 1977) Assumptions:Proxy model assumptions. Preprocessing:None. Type:Selection. Approach:Greedy multivariate (forward). Criterion:Holdout performance of the proxy model. Implementation:We use LGBM as the proxy model. At each step, the feature with the highest importance under the LGBM proxy refit on the current subset including this feature is added. B.5OVERVIEW OF ALL INVESTIGATED FEATURE SELECTION METHODS Table B.2 provides a complete overview of all investigated methods, including the source links for a clear distinction and the final decision for inclusion or exclusion. Table B.2: Overview of all investigated feature selection methods. We list all methods with their source links, provide the year, and the number of appearances across the relevant literature (Section 3.2). In the Decision column, we use Yes if the method is included in SelectArena or list the reason for exclusion (Citations if there are fewer than three citations, Variation if the method is a variation of another method, Wrong Domain if the method is not suitable for tabular data, and Not task-agnostic if the method is not suitable for all tasks). We additionally mark some methods with Yes, future work to indicate that their implementations are ongoing. In all cases, these implementations are missing differential entropy logic. Feature Selection Method NameYear# appearancesDecision AACO20241Citations ACO 120041Citations ACO 220101Citations ACO 320121Citations ACO 420141Citations AISCBR20111Citations 28 Table B.2: Overview of all investigated feature selection methods (continued). Feature Selection Method NameYear# appearancesDecision AMIFS20041Citations, Variation ANOVA F-Test19896Yes AUMFS20131Wrong Domain, Citations Accuracy20063Yes Adaptive Lasso20122Citations, Variation Alpha-Investing20052Citations Attention Map Importance20211Citations, Variation BAB*19931Citations BRIDGE regularization20081Citations BSS/WSS20021Citations BW-Ratio20091Citations Balanced Error Rate20061Citations Ball Correlation (Bcor-SIS)20181Citations Bayesian20011Citations Bivariate20021Citations Bolasso20081Citations, Variation Boruta20101Citations, Variation C4.519931Citations CAE-S20191Citations CART19844Yes CAWOA-NRS20260Citations CEFS+20250Citations CFS (Hall)19998Not task-agnostic CMIFS20111Citations, Variation CMIM20045Not task-agnostic CPFS20101Citations CXPlain20191Citations Categorical Concordance Index (CI-SIS)20231Citations Category-Adaptive Variable Screening (CAS)20191Citations Combined Ant Colony and Differential Evolution FS Algorithm 20081Citations Concordance Index (CSIS)20241Citations Conditional Distance Correlation (CDCSIS)20151Citations Conditional Infomax Feature Extraction20051Citations Conditional Perm. Test20081Citations Conditional SIS20121Citations Consistency20003Not task-agnostic Constraint score20081Citations Correlation Coefficient20141Citations DE 220141Citations DIME-DG20231Citations DISR20065Not task-agnostic DeepFS20151Citations Distance Correlation (DC-SIS)20121Citations Distinguishing Feature Selector20121Citations EDDI-G20191Citations EHOFS20250Citations EUFS20151Citations, Variation Elastic net20053Yes Equal Information Gain19661Citations, Variation Evolution Strategy FS20091Citations Exclusive Lasso20101Citations, Variation Expected Cross Entropy20091Citations Extremal Perturbations (EP)20191Citations FCBF20035Variation FIDO-CA20181Citations FIGFS20250Citations FOCUS19941Citations FS-P20061Citations FSDS20151Wrong Domain, Citations FSRDE20260Citations 29 Table B.2: Overview of all investigated feature selection methods (continued). Feature Selection Method NameYear# appearancesDecision Feature Similarity (Mahalanobis)19361Citations Feature Weighting k means20031Citations Feature selection for sparse clustering20101Citations Fisher criterion20122Citations, Variation Fisher score19366Not task-agnostic GA 119891Citations GA 220101Citations GA 320141Citations GDCOV20081Wrong Domain, Citations GDFS-DG20231Citations GFLasso20091Citations, Variation GP 120061Citations GP 220101Citations GP 320121Citations GSBE19971Citations GSFS19971Citations Gain Ratio19863Yes Gain Ratio Cost20031Citations, Variation Gamma20011Citations Generalized SIS20091Citations Genetic Algorithm (Siedlecki)19892Citations Genetic Algorithm (Yang)20022Citations Gini19126Not task-agnostic Grafting20032Citations, Variation Graph FS (GOSCAR)20122Citations, Variation GreedyFS20111Citations Group Lasso20203Variation HSIC Lasso20181Citations, Variation Heuristic Search (GA)19811Citations Hilbert-Schmidt Independence Criterion (HSIC)20221Citations IAIRS20140Citations ICS-RCFS20260Citations ID319861Citations IF20031Citations IME (2009)20091Citations IME (2010)20101Citations INTERACT20092Not task-agnostic INVASE20181Citations ISFS20260Citations Impurity20063Not task-agnostic Impurity Euclid20211Citations, Variation Impurity Hellinger20211Citations, Variation Inconsistency criterion19961Citations Information Gain19867Not task-agnostic Information Gain Text20061Wrong Domain Informative Fragment20031Citations Interaction Capping20112Citations Interaction Gain FS20081Citations JAFA-MFRL20181Citations JIM20211Citations, Variation JMI19995Not task-agnostic JMIM20152Citations Jaccard Index20081Citations KernelSHAP20221Citations, Variation Koller-Sahami metric (KS)19961Citations Kolmogorov Filter (Kfilter)20141Citations Kruskal-Wallis20152Citations, Variation L2X20181Citations LFS (lazy feature selection20091Citations LIME (Images)20191Wrong Domain, Citations LIME (Tabular)20211Citations 30 Table B.2: Overview of all investigated feature selection methods (continued). Feature Selection Method NameYear# appearancesDecision LLM-Select20250Citations LS (Flip Swap) first improve/0Citations LS (Flip) best improve/0Citations LSSSMV-FS20260Citations LUFS20121Wrong Domain, Citations Laplacian score20055Yes Lasso (L1)19968Yes Lasso Net20191Citations, Variation LinkedFS20121Wrong Domain, Citations Localized Feature Selection Based on Scatter Sepa- rability (LFSBSS) 20081Citations Logistic Regression Coefficients20021Citations LossSHAP20201Citations, Variation MA-C20101Citations MAFESE20241Citations MBEGA20071Citations MCFS20103Variation MDL19942Citations, Variation MDL Lamp20031Citations, Variation MI (Battiti)19946Yes MI (Guyon)20032Variation MIFS-U20021Citations, Variation M20171Citations MOPSO20250Citations MRMR200512Yes MVFS20131Wrong Domain, Citations Markov blanket19963Yes Martingale Difference Correlation (MDC-SIS)20131Citations Maximum Correlation (MC-SIS)20161Citations Maximum Mean Discrepancy MMD20221Citations Md (measure of monotone dependence)20101Citations Mean-Variance (MVSIS)20151Citations Meaningful Perturbations (MP)20171Citations MinRed20061Citations Minimal image representation (MIR)20141Wrong Domain, Citations Modified Perceptron by Truncation for OFS20141Citations MsFDSs20250Citations Multi-response rank canonical correlation (mRCC)20211Citations Multi-viewFS20250Citations Multinomial Logistic Regression20061Citations Myopic ReliefF19941Citations, Variation NB19661Citations NDFS20122Citations NJMIM20152Citations, Variation NP (node pruning)19941Citations NetFS20161Wrong Domain, Citations Nonparametric Independence Screening (NIS)20111Citations ODIN-MBRL20191Citations ODIN-MFRL20191Citations OFS20141Wrong Domain, Citations OSCAR20081Citations, Variation OSFS20102Wrong Domain, Citations ObDI-FS20260Citations Occlusion20141Citations Odds Ratio20011Citations Original (no FS)nan0Not task-agnostic Orthogonal Matching Pursuit19931Citations PAM20021Citations PCA20141Citations PSO 120101Citations PSO 220131Citations 31 Table B.2: Overview of all investigated feature selection methods (continued). Feature Selection Method NameYear# appearancesDecision PSO 320021Citations PT-S20011Citations Pairwise SIS (PSIS)20151Citations Pearson Correlation Coefficient20084Not task-agnostic Permutation Test20011Citations PredDiff20171Citations Projection Correlation (PC-Screen)20191Citations QII20161Citations Quantile Correlation (QCSIS)20161Citations REAL-X20211Citations RF importance20018Yes RISE20181Citations ROC20011Citations Random FS20261Not task-agnostic Random FS (fixed size)20262Not task-agnostic Random permutations20011Citations Randomised Lasso20081Citations, Variation Rank products20041Citations Regression20011Citations Relief19929Variation Relief Kukar19992Citations, Variation Relief-A19940Citations, Variation Relief-B19940Citations, Variation Relief-C19940Citations, Variation Relief-D19940Citations, Variation ReliefF199710Yes ReliefF averaged costs20031Citations, Variation ReliefF avg probablity20031Citations, Variation ReliefF cost-sensitive sampling20031Citations, Variation ReliefF distance20031Citations, Variation ReliefF equal K20031Citations, Variation ReliefF expected costs20031Citations, Variation ReliefF expected probability20031Citations, Variation ReliefF expected rank20031Citations, Variation ReliefF squared distance20031Citations, Variation Repeated Elastic Net Technique (RENT)20211Citations, Variation Ridge (L2)20221Citations Robust Rank Correlation (RRCS)20121Citations SAGE (Shapley Additive Global importancE)20201Citations, Variation SBE (sequential backward elimination)20258Yes SFBE19942Variation, Citations SFFS19943Variation SFS (sequential forward selection)202510Yes SHAP20171Citations, Variation SHAPIQ20240Yes SIT-BY20221Citations SOA20091Citations SOD-LSC20141Citations SPFS-SFS20131Citations SPVIM20201Citations SVM-RFE20026Variation Shapicant20201Citations, Variation Shapley Values19531Citations Sparse Group Lasso20141Citations, Variation Sparse-based filter method20151Wrong Domain, Citations Spearman Correlation Coefficient20082Citations Spectral FS (SPEC)20073Variation Stability Selection20081Citations Stabl Lasso20241Citations, Variation Sure Independence Screening (SIS)20081Citations Sure Independent Ranking and Screening (SIRS)20101Citations 32 Table B.2: Overview of all investigated feature selection methods (continued). Feature Selection Method NameYear# appearancesDecision Symmetrical Uncertainty19885Not task-agnostic TNOM20001Citations Trace Ratio20081Citations, Variation Tree structured group Lasso20101Citations, Variation Tree Lasso20231Citations, Variation Tree-guided group Lasso20101Citations, Variation TreeSHAP20201Citations, Variation Ts-repso20250Citations UDFS20112Citations, Variation USC20031Citations USFS20151Wrong Domain, Citations Uniform Accuracy20211Citations, Variation Uniform Gini20211Citations, Variation Uniform Information Gain20211Citations, Variation Univariate Predictors20031Citations Weighted Leverage Score (WLS)20211Citations Weighted Log Likelihood Ratio20001Citations Wilcoxon rank sum20011Citations WrapperSubsetEval20051Citations XCSCFˆ220141Citations auc20202Citations, Variation bee colony approach FS20151Citations bumble bees mating optimization FS20141Citations chi squared19956Not task-agnostic dist angle20211Citations dist euclid20212Citations dist hellinger20211Citations distance20141Citations entropy20141Citations equal gini20211Citations, Variation equal hellinger20211Citations f_regression20252Citations limma20041Citations, Variation low variance20181Citations, Variation mRMR-MOBSTA20260Citations max of minority20131Citations oneR19933Not task-agnostic sam20011Citations, Variation signal-to-noise (SNR)20071Citations sum minority20131Citations t-test20115Not task-agnostic term frequency–inverse document frequency20201Citations true positives (TP)20071Citations univariate model score20161Citations variance20202Citations xgb importance20161Citations, Variation CDATASETS C.1DATASET SOURCES Table C.1 lists the 73 articles, comprising feature selection surveys and benchmarks, the papers introducing our selected methods, and related work from explainable AI, that we reviewed to assemble the pool of 368 candidate datasets. 33 Table C.1: Reviewed dataset sources. The 73 articles screened for real-world tabular datasets, in alphabetical order. Agarwal et al. (2022)Kira & Rendell (1992)Robnik-Šikonja & Kononenko (2003) Bakhmach et al. (2026)Kohavi & John (1997)Ruiz-Ramírez et al. (2023) Battiti (1994)Koller & Sahami (1996)Saeys et al. (2008) Belaid et al. (2023)Kononenko et al. (1997)Schütz et al. (2025) Bennasar et al. (2015)Kou et al. (2020)Sithakoul et al. (2024) Bolón-Canedo et al. (2013)Lee & Lee (2006)Smyth (2004) Bommert et al. (2020)Li et al. (2012)Stiglic et al. (2012) Bouchlaghem et al. (2022)Li et al. (2017)Umar et al. (2025) Breiman (2001)Li et al. (2018)Vidal-Naquet & Ullman (2003) Brown et al. (2012)Li et al. (2022)VonKaenel et al. (2026) Cai et al. (2010)Lin & Tang (2006)Wang et al. (2013) Chandrashekar & Sahin (2014) Liu & Setiono (1995)Wang et al. (2015) Chen (2016)Liu et al. (1996)Wang et al. (2017) Cherepanova et al. (2023)Liu et al. (2021)Yang et al. (2011) Covert et al. (2020)Liu & Motani (2025)Yang et al. (2012) Dash & Liu (2003)Lundberg & Lee (2017)Yu & Liu (2003) Erickson et al. (2025)Ma & Huang (2008)Yu & Liu (2004) Gao et al. (2017)Ma et al. (2024)Zhang et al. (2008) Gorishniy et al. (2021)Meyer & Bontempi (2006) Zhao & Liu (2007) Grinsztajn et al. (2022)Miao & Niu (2016)Zhou et al. (2005) Guyon & Elisseeff (2003)Peng et al. (2005) Guyon et al. (2007a)Perkins et al. (2003) Hall (1999)Reunanen (2003) He et al. (2005)Heinze et al. (2018) Holte (1993)Hooker et al. (2019) Hopf & Reifenrath (2021)Jain & Zongker (1997) Khafaga et al. (2023) C.2DATASET CURATION CRITERIA (1) Unique predictive task definition:A task with a given name should only exist once in SelectArena. Tasks with the same or similar name and/or different shapes require careful investigation to recover the original dataset version and avoid duplicates or inconsistent variants. (2) Tabular data (e.g., no text or image data):We restrict SelectArena to genuinely tabular datasets and therefore exclude datasets derived from text, image, or other modalities. Without domain- specific expertise, such datasets cannot reliably be treated as tabular data and would compromise the comparability and validity of benchmark results. (3) Minimum 10 features for meaningful selection:Cardinality-constrained feature selection (see Section 3.1) requires a sufficient number of features to define meaningful cardinality constraints, which is why datasets with fewer than 10 features are excluded. (4) Real-world origin (no synthetic data):The goal of SelectArena is to provide guidance for practitioners. Despite advances in synthetic data generation, synthetic datasets are not representing real-world distributions and may distort the interpretation of benchmark results. (5) IID data suitable for random splitting:All datasets in SelectArena are evaluated using random splits. Datasets containing temporal dependencies, grouped observations, or other structures requiring specialized splitting strategies are excluded to avoid information leakage and invalid performance estimates. (6) Genuine predictive task:Each dataset must contain a valid predictive target. We exclude datasets where targets are artificially constructed, randomly assigned, or otherwise not intended for predictive modeling, as can occur in certain scientific data repositories. (7) No ethical concerns with the predictive task:Datasets and their associated tasks are carefully reviewed for ethical concerns before inclusion. The well-known case of the Boston Housing dataset illustrates the importance of critically examining datasets and predictive tasks prior to making them broadly accessible to the community. 34 (8) Accessible source data:Datasets referenced in the literature must be available and usable. During the curation process, we experienced several cases where datasets were not publicly available (e.g., Bakhmach et al. (2026)) or the source links outdated (e.g., Guyon & Elisseeff (2003)). We therefore exclude datasets that could not be accessed, verified, and reliably reused. C.3DATASET CURATION INSIGHTS •Feature selection and machine learning methods are routinely applied across a wide range of datasets, regardless of their original modality. In our opinion, tabular data derived from, e.g., texts or images cannot be safely treated as standard tabular data without domain-specific expertise. • Feature selection is typically of limited practical relevance for datasets with fewer than 10 features, as only few candidate features are available for selection. In addition, defining meaningful budgets for cardinality-constrained feature selection (see Section 3.1) requires a sufficiently large feature space. •The IID assumption receives limited attention in the literature, even if an inappropriate splitting strategy can cause information leakage and misleading results (e.g., in case of temporal dependencies or grouped observations). •Sensitive features are frequently included in benchmarks without critical evaluation. Datasets from ethically sensitive domains appear more prevalent within the XAI-focused feature selection literature. In our view, both the predictive task and the presence of sensitive attributes should be carefully considered when deciding whether a dataset is appropriate for benchmarking. • High-dimensional datasets are typically accompanied by limited sample sizes, e.g., in biomedical applications where thousands of genes may be measured for only a small number of patients. These settings are especially prone to overfitting and overtuning. •Determining whether a dataset represents real-world or synthetic data can be challenging, particularly for heavily preprocessed datasets where feature names are anonymized and the original source is no longer identifiable (e.g., AutoML challenge data). •In some cases, we observed an arbitrary choice of target variables. This issue commonly arises in scientific discovery datasets that were not originally designed for predictive mod- eling, where targets are later selected from existing features, or artificially constructed, resulting in diverging variants of the same dataset. C.4OVERVIEW OF SELECTED DATASETS Table C.2 provides details on the included datasets. Table C.2: Overview of selected datasets. We list all datasets included in SelectArena, the number of features, samples and classes, as well as the task type and license for the usage of the datasets. Dataset# Features# Samples# ClassesTaskLicense type allstate-claims-severity (Ferguson et al., 2016) 131188318/RegressionPublic ancestry-study(Ruiz-Ramírez et al., 2023) 1056357MulticlassPublic anneal (Unknown, 1990)398985MulticlassCC-BY-4 aps-failure (IDA2016Challenge, 2016) 171760002BinaryCC-BY-4 audiology-diagnosis (Bareiss et al., 1990) 6919924Multiclass/ automobile (Schlimmer, 1985)252057OrdinalCC-BY-4 bad-customer-detection (Podsyp, 2020) 1417232BinaryCC0: Public Domain bank-customer-churn(Topre, 2022) 11100002BinaryPublic 35 Table C.2: Overview of selected datasets (continued). Dataset# Features# Samples# ClassesTaskLicense type bank-marketing (Moro et al., 2014) 13452112BinaryCC-BY-4 bioresponse (Hamner et al., 2012)177737512BinaryCompetition body-density-prediction (Penrose et al., 1985) 14252/RegressionPublic brasilian- houses (de Oliveira Farias & Fontes, 2020) 1310692/RegressionPublic domain churn (Marcoulides, 2005)2050002Binary MIT License clock-protein-period (Gul et al., 2021) 1092902BinaryCC-BY-4 clock-protein-toxicity (Gul et al., 2021) 11121712BinaryCC-BY-4 coil-2000-insurance- policies (van der Putten & van Someren, 2000) 8598222Binary/ colon-tumor (Alon et al., 1999)1992622BinaryPublic credit-approval (Quinlan, 1987)166902BinaryPublic credit-card-clients-default (Yeh & hui Lien, 2009) 24300002Binary/ customer-satisfaction-in- airline (Yakhyojon, 2023) 221298802BinaryCC0: Public Domain drug-induced-autoimmunity- prediction (Huang et al., 2025) 1785972BinaryCC-BY-4 forest-fires (Cortez & Morais, 2007) 13517/RegressionPublic framingham-heart-study (Bhard- waj, 2022) 1642402BinaryUnknown give-me-some-credit (Fusion & Cukierski, 2011) 121500002BinaryPublic hazelnut-spread-contami/t- detection-10GHz (Urbinati et al., 2020) 3124002BinaryCC-BY-SA heart-disease-cleveland (Detrano et al., 1989) 143032BinaryCC0: Public Domain heart-disease-hungary (Detrano et al., 1989) 142942BinaryCC0: Public Domain heart-disease-switzerland(De- trano et al., 1989) 131232BinaryCC0: Public Domain heart-disease-va-long-beach (De- trano et al., 1989) 142002BinaryCC0: Public Domain heloc (Oliabev, 2021)24104592BinaryPublic hepatitis-survival- prediction(Efron&Gong, 1981) 201552BinaryCC-BY-4 hiva-agnostic(Guyonetal., 2007b) 148142292BinaryPublic homesite-quote-conversion (Dar- rel et al., 2015) 2962607532BinaryCompetition horse-colic-survival (McLeish & Cecile, 1989) 213443MulticlassCC-BY-4 hr-analytics (Arashnic, 2021)13191582BinaryCC0: Public Domain in-vehicle-coupon- recommendation (Wang et al., 2017) 24126842BinaryCC-BY-4 ionosphere (Sigillito et al., 1989)343512BinaryCC-BY-4 kdd-cup-09-appetency(Guyon et al., 2009) 212500002BinaryCC0: Public Domain labor-negotiations (Matwin, 1988)17572BinaryCC-BY-4 36 Table C.2: Overview of selected datasets (continued). Dataset# Features# Samples# ClassesTaskLicense type leukemia-allaml (Golub et al., 1999) 7072722BinaryPublic ljubljana-primary-tumor (Zwitter & Soklic, 1987) 1830222MulticlassCC-BY-4 lung-cancer (Bhattacharjee et al., 2001) 126012035MulticlassPublic lymphography (Zwitter & Soklic, 1988) 191424MulticlassCC-BY-4 marketing-campaign (Unknown, 1990) 2622402BinaryPublic mechanisms-of-action (inversion et al., 2020) 206238142MulticlassCompetition miami-housing (Bourassa et al., 2021) 2410459/Regression C-BY-NC- SA mic (Golovenkin et al., 2020)11217002MulticlassCC-BY-4 naticusdroid-android- permissions-dataset(Mathur et al., 2021) 87293322BinaryCC-BY-4 nci-ovarian-cancer (Petricoin et al., 2002a) 151482532BinaryPublic nci-pancreatic-cancer (Hingorani et al., 2003) 67711812BinaryUnknown nci-prostate-cancer(Petricoin et al., 2002b) 151543222BinaryUnknown online-shoppers-purchasing- intention-dataset (Sakar et al., 2019) 18123302BinaryCC-BY-4 otto-group-product-classification- challenge (Bossan et al., 2015) 94618789MulticlassCompetition polish-companies- bankruptcy (Zieba et al., 2016) 6557902BinaryCC-BY-4 porto-seguro (Howard et al., 2017)385952062BinaryPublic predict-students-dropout-and- academic-success (Martins et al., 2021) 3744243MulticlassCC-BY-4 prostate-cancer (Singh et al., 2002)126011022BinaryPublic pva-revenue-prediction- kddcup98 (Parsa, 1998) 479144095/RegressionPublic qsar-biodeg (Mansouri et al., 2013) 4210552BinaryCC-BY-4 qsar-oral-toxicity(Unknown, 2019) 102589922BinaryCC-BY-4 qsar-tid-11 (Olier et al., 2018)10255742/Regression C0: Public Domain santander-customer-transaction- prediction (Piedra et al., 2019) 6012000002BinaryCompetition sdss-17 (Abazajian et al., 2009)121000003Multiclass Public Domain smoking-lung-cancer (Spira et al., 2007) 222161872BinaryPublic soybean-large (Michalski & Chi- lausky, 1980) 3630618MulticlassCC-BY-4 srbct-prediction (Khan et al., 2001) 2309834MulticlassPublic superconductivity(Hamidieh, 2018) 8321263/RegressionCC-BY-4 taiwanese-bankruptcy- prediction (Liang et al., 2016) 9668192BinaryCC-BY-4 thyroid-discordant(Quinlan, 1987) 2737115Multiclass/ 37 Table C.2: Overview of selected datasets (continued). Dataset# Features# Samples# ClassesTaskLicense type wine-quality (Cortez et al., 2009)126497/RegressionCC-BY-4 C.5CARDINALITY CONSTRAINTS As presented in section 3.3, we calculate the cardinality constraintkbased on the feature dimension- alitypfollowing one of the two formulas, conditioned onp. Ifp≤ 100, we consider the dataset to have a low feature dimension and we spread the constraintklinearly across the feature set size. The datasets with up to 100 features come from very different domains, e.g. marketing, life sciences, business. Since practical tasks in these domains can range from highly sparse feature selection to settings where retaining dozens or nearly 100 features is feasible, we use a linear cardinality spacing to provide balanced coverage across this range of plausible practitioner requirements. We consider datasets with more than 100 features to have high feature dimension and we spread the constraintk in an exponential way. These datasets come primarily from biological and medical domains, where feature selection tasks often aim to identify substantially smaller subsets of relevant features rather than retain most of the original feature set. We therefore allocate more of the evaluated budgets toward smaller feature subsets, while still including larger budgets to cover less restrictive selection settings. Formally, we calculate cardinality constraint k for j ∈1,..., 5 as following: k =⌈p× j/6⌉ if p≤ 100, k =⌈p j/6 ⌉ otherwise. We summarize the distribution of the features across the cardinality indices j in Table C.3. Table C.3: Number of selected featureskacross the 71 benchmark datasets, per cardinality indexj. Cardinality idx jMin kMax k Linear (p≤ 100) min–max k Exp. (p > 100) min–max k Mean k 12162–163–64.7 24314–315–2911.1 351505–4711–15027.4 477917–6223–79187.3 5941909–7848–4190344.5 DBENCHMARKING DETAILS AND EXTENDED RESULTS In this section, we present the benchmarking details for predictive performance (D.2), stability (D.3), and validity (D.4). Finally, we provide the raw predictive performance results (D.5). D.1OPEN-SOURCE BENCHMARKING The code of our method implementations and evaluation pipeline is publicly available on GitHub (under construction). Additionally, we publish the curated versions of our datasets. D.2PREDICTIVE PERFORMANCE In the following paragraphs, we present the calculation and results for the Elo, provide details on the Pareto calculation, and show the results for improvement over random and runtime per 1k features. Additionally, we provide an ablation study on the impact of tuning and discuss the statistical significance of our findings. Evaluation metrics.We evaluate the predictive performance of the downstream models using area under the receiver operating characteristic curve (ROC AUC) for binary classification, log-loss 38 for multi-class classification, and root mean squared error (RMSE) for regression, following other popular benchmarks (Erickson et al., 2025; Gijsbers et al., 2023). We do not use other metrics, that might seem to be more suited for imbalanced datasets: ROC AUC is well-suited to comparing performance across heterogeneous, often imbalanced, datasets as it is invariant to class prevalence. We do not use the area under the precision-recall curve (PR AUC), a popular alternative for imbalanced data, as it favors improvements on subpopulations with higher prevalence (McDermott et al., 2025). ROC AUC is also threshold-free, i.e., it does not require fixing a decision thresholdτfor model predictionsˆy = 1[ ˆp(x) > τ ], whereˆp(x)is the predicted probability of the positive class. This avoids the problem of defining costs for different error types (false positive or false negative), which depend on the task and application context. Log-loss is a strictly proper scoring rule which allows evaluating how well the predicted distribution matches the true distribution as measured on the held-out set. Similar to ROC AUC, it does not commit to a fixed misclassification cost, which we cannot specify in advance for a benchmark spanning heterogeneous datasets and application contexts. RMSE plays an analogous role to log-loss as it is minimized in expectation only by the true conditional mean which implies that a method cannot improve its score other than by predicting better. It additionally keeps per-dataset results directly interpretable by retaining the units of the target variable (contrary to, e.g., the mean squared error). Elo Rating.We use an Elo-like rating for comparing the included feature selection methods. The Elo rating system has originally been designed to rate chess players and therefore its nature is time-dependent, i.e. the order of the matches matters as each update uses a player’s current rating to compute the expected outcome. More formally, the rating of player A is calculated as: r A ← r A + K (S A − E A ), E A = 1 1 + 10 (r B −r A )/400 ,(1) whereK = 32is a fixed scaling factor,S A ∈0, 0.5, 1is the actual outcome andE A is the expected outcome. This gives a convenient interpretation of rating differences: a gap of 150 points corresponds to a win probability of about 70% for the stronger player, and a gap of 400 points to≈10-to-1 odds (91%). For our benchmark setup, this system is not appropriate because 1) methods, contrary to players, are static and do not improve over time; 2) at the time of evaluation, we have access to all "matches". Therefore, following Chatbot Arena (Chiang et al., 2024), we adopt a Bradley-Terry rating system, of which Elo is a special case. We keep the "Elo" name as we scale the ratings in such a way that they can be interpreted as the Elo ratings. In the Bradley-Terry model, we calculate the outcome of a pairwise comparison of the methods between each unique combination of downstream model, and budget with scores per combination averaged over seeds. Formally, the probability of methodibeing better than methodjis calculated as: P (i > j) = p i p i + p j = e β i e β i + e β j = 1 1 + e β j −β i (2) wherep i = e β i is the score function proposed by the model’s authors and also the one we adopt in this benchmark. The scoreβ i is in natural-log-odds units. We rescale them to the conventional Elo scale byr i = (β i − β Random )· 400 ln 10 + 1000, which anchors random feature selection at 1000 and preserves the standard Elo interpretation. Within each dataset, we define a setting as a combination of budget and downstream model, with the metric averaged over seeds. In each setting, every pair of methods forms a match, where win is assigned to a method with better score and ties are counted as half-wins. We estimateβby maximum likelihood on all matches. To quantify uncertainty, we use a cluster bootstrap over datasets: since all settings of a dataset share dataset-specific signal, their outcomes are correlated, so we resample the 71 datasets with replacement and refit the model on the matches of the sampled datasets. Over 1000 resamples, we report the 2.5th and 97.5th percentiles of each method’s ratingr i as its 95% confidence interval. Results are shown in Table D.1. Pareto Details.Comparing metric errors across datasets of different task types can either be handled by ranking methods per dataset, as we did in the Elo calculation, or by normalizing the 39 Table D.1: Elo rating. We report the score per method along with 95% confidence intervals from 1000 cluster bootstrap resamples at the dataset level. Higher is better. MethodElo rating95% CI RFImportance1305.8[1259.6, 1356.2] CART1262.4[1214.3, 1314.2] ElasticNet1249.4[1203.0, 1301.6] MI1245.8[1207.2, 1290.1] F-Test1226.7[1184.1, 1272.2] Lasso1224.1[1180.0, 1274.8] MarkovBlanket1217.6[1179.8, 1262.8] (R)ReliefF1195.7[1145.4, 1250.5] mRMR1191.5[1150.6, 1237.5] GainRatio1181.5[1146.1, 1222.1] SFS1176.5[1136.9, 1226.4] SBE1167.4[1119.5, 1224.8] LOCO1149.5[1106.0, 1197.7] Random1000.0[1000.0, 1000.0] LaplacianScore965.4[ 913.9, 1009.7] errors by a baseline. We calculate and compare the improvementI (m,d) of a methodmon a datasetd in % using the metric error of a methodErr (m,d) and the metric error of random feature selection Err (r,d) . The errors are averaged across budgets and models. I (m,d) = Err (r,d) − Err (m,d) Err (r,d) · 100(3) The runtime per 1k featurest 1k is calculated by using the needed time of a feature selection method on a dataset, budget, and model, t (d,b,m) , and the number of features of a dataset n (d) : t 1k = t (d,b,m) n (d) · 1000(4) By normalizing the time and the features per dataset, the comparison across datasets is possible and we obtain the results as shown in Table D.2. Table D.2: Predictive performance vs. runtime. We report the mean improvement over random and mean runtime per 1k features for all benchmarked feature selection methods. Name Improvement over random (%) Time per 1k features (s) RFImportance26.2345.19 MI23.8051.24 CART23.7815.71 F-Test22.760.79 ElasticNet21.882400.69 mRMR21.6756.50 MarkovBlanket21.2421.30 Lasso20.902370.68 GainRatio19.5314.26 (R)ReliefF18.5724442.87 SFS18.1827903.08 LOCO12.986304.68 SBE11.4027018.73 Random0.000.006 LaplacianScore−18.584.60 Significance analysis.We perform the significance analysis using autorank (Herbold, 2020). We consider correlations at the level of budgets, downstream models, and seeds. We prepare the 40 autorank input so that each row represents an independent measurement and columns correspond to populations we want to compare. We use rank as a task-agnostic metric to compare feature selection methods across regression and classification datasets. First, we aggregate the results over three seeds and then we rank the methods for each combination of dataset, budget, and downstream model, assigning average rank to ties. Then, for each dataset and method, we average the rank over budgets and downstream models. Cells in the resulting matrix represent average rank of a method on a given dataset. To avoid relying on parametric assumptions, autorank applies the non-parametric Friedman test fol- lowed by the post-hoc Nemenyi test, that reported significant difference between methods’ mean rank if it exceeded the critical distance of 2.546. The resulting critical difference diagram (CDD) is shown in Figure D.1a: There are four groups of non-distinguishable methods linked with the horizontal bar. We report significant difference between five pairs: RFImportance and SBE, RFImportance and LOCO, RFImportance and (R)ReliefF, CART and LOCO, ElasticNet and LOCO. Additionally, all 13 methods significantly beat the random baseline and LaplacianScore. We additionally show CDDs for five cardinality indices in Figure D.1b-D.1f. For the lowest budget index (Figure D.1b), we observe one larger group of undistinguishable methods that is significantly better only than random baseline and LaplacianScore. As the budget grows, the number of groups increases and so does the rank spread. RFImportance consistently ranks in top-3 methods, CART ranks better in smaller budgets, while F-Test ranks better in bigger budgets 123456789101112131415 LaplacianScore Random LOCO SBE (R)ReliefF GainRatio mRMR SFS MarkovBlanket F-test MI Lasso ElasticNet CART RFImportance CD (a) Aggregated over budgets. 123456789101112131415 LaplacianScore Random SBE LOCO (R)ReliefF GainRatio mRMR Lasso SFS F-test MI ElasticNet RFImportance MarkovBlanket CART CD (b) Budget idxj = 1. Mean number of featuresk=4.7. 123456789101112131415 LaplacianScore Random LOCO SBE (R)ReliefF SFS mRMR GainRatio F-test MI MarkovBlanket Lasso ElasticNet RFImportance CART CD (c) Budget idxj = 2. Mean number of features k=11.1. 123456789101112131415 LaplacianScore Random LOCO SBE mRMR SFS (R)ReliefF GainRatio MarkovBlanket F-test MI Lasso CART ElasticNet RFImportance CD (d) Budget idxj = 3. Mean number of features k=27.4. 123456789101112131415 LaplacianScore Random LOCO SBE SFS mRMR GainRatio MarkovBlanket (R)ReliefF ElasticNet F-test CART MI Lasso RFImportance CD (e) Budget idxj = 4. Mean number of features k=87.3. 123456789101112131415 LaplacianScore Random LOCO GainRatio SFS SBE MarkovBlanket (R)ReliefF mRMR Lasso ElasticNet CART MI F-test RFImportance CD (f) Budget idxj = 5. Mean number of features k=344.5. Figure D.1: Critical difference diagrams. Average ranks per dataset with critical distance= 2.546. Tuning LGBM.We assess the impact of tuning the downstream model on predictive performance. We focus on LGBM, the model with the largest tunability (Erickson et al., 2025), via a random 41 search with 10 hyperparameter configurations. For each feature selection method, we pick the configuration with the lowest metric error averaged across budgets and datasets. The tuned model is reported alongside the other downstream models in Figure D.2. We use the per-task normalized score (Erickson et al., 2025),s i,t = max(0, ( ̃e t − e i,t )/( ̃e t − e ⋆ t )), wheree ⋆ t and ̃e t are the best and median errors on taskt. The best method on each task scores1, the median scores0, and worse-than-median methods are floored at0. On average, tuning improves the normalized score by 24.7%over the default configuration. This suggests that practitioners interested in boosting predictive performance on the downstream task should consider tuning their predictors in addition to applying feature selection. (R)ReliefF CART ElasticNet F-Test GainRatio LOCO LaplacianScore Lasso MI MarkovBlanket RFImportance Random SBE SFS mRMR 0.0 0.1 0.2 0.3 0.4 0.5 Normalized Score Model LGBM LGBM (Tuned) LM RF RealMLPModel TabICLv2 Figure D.2: Impact of tuning. Normalized scores per method per model, averaged over datasets and budgets. Tuned LGBM (striped blue) outperforms the default (solid blue) by a clear margin. Random fallback.In SelectArena, besides the cardinality constraint, we impose a time limit of one hour for each: the feature selection and the downstream model training and inference. This limit reflects the practical trade-off between predictive performance and computational cost. Improvements in downstream performance may be of limited value if they require prohibitively expensive feature selection or model evaluation. We use one hour as a common computational budget to capture this trade-off consistently across methods. Where supported by the implementation, this limit is enforced by interrupting the method; otherwise, the feature selection stage is allowed to complete but is recorded as a timeout if it exceeds the limit. When feature selection is interrupted, we apply a mechanism called random fallback to keep the cardinality fixed across runs. Specifically, random fallback either fills the remaining feature subset with randomly selected features or randomly subsamples the current subset down to the cardinality constraint. The latter applies to SBE and Markov Blanket, which start with the full feature set and iteratively remove features until the target cardinality is reached. This allows timed-out runs to remain part of the evaluation. Discarding timed-out runs would instead result in different methods being evaluated on different datasets, with slower methods disproportionately evaluated on lower-dimensional and potentially easier datasets. Random fallback therefore provides a practical mechanism for maintaining comparability under both cardinality and time constraints. Table D.3 summarizes the timeout and random fallback rates. Timeouts are frequent for the search methods SBE (46.9%) and SFS (44.8%), less frequent for LOCO (23.5%) and Relief(F) (17.0%), and rare for Lasso (4.0%), mRMR (3.4%), and ElasticNet (1.4%). All other methods never hit the time limit. The discrepancy between the timeout and fallback rates has two causes. First, for SBE, SFS, LOCO, and Relief(F) some runs produce a complete set of features (no fallback) in a final step that starts within the time limit but ends after it. Second, Lasso and ElasticNet always run to completion as their scikit-learn interface does not allow interruption. Given their low timeout rates, we accept this as a minor deviation from our time-constraint framing of the benchmark. To measure the impact of random fallback on predictive performance, we rerun experiments without it on a single seed (5budget indices× 5downstream models× 71datasets) and present the results in Table D.3. As expected, the∆(difference between the improvement over Random for no fallback and fallback cases) is largest for methods that trigger the fallback most often, i.e. SBE, SFS, and LOCO. Without fallback, SBE returns feature subsets larger than the cardinality constraint when it reaches 42 Table D.3: Impact of random fallback on run completion and improvement over RANDOM. A run is a unique combination of dataset, budget, and downstream model; each method has 1,775 runs. Timeout counts runs exceeding the one-hour limit. Fallback counts runs completed or reduced through random selection after a timeout. Improvement over RANDOM (Apppendix D.2) normalizes performance across tasks with different error metrics. We report the mean for the default pipeline (Fallback) and the ablated one (No fallback), with ∆ = No fallback− Fallback. TimeoutFallbackImprovement over RANDOM Methodn%n% Fallback No fallback∆ (p) SBE832 46.9825 46.510.0128.06 +18.05 SFS795 44.8778 43.816.96−1.99−18.95 LOCO417 23.51347.511.5810.11−1.47 (R)ReliefF301 17.000.019.2319.22−0.01 Lasso714.000.020.9520.95+0.00 mRMR603.4603.421.9721.96−0.01 ElasticNet251.400.022.9122.91+0.00 CART00.000.024.2124.21+0.00 F-Test00.000.023.7123.71+0.00 GainRatio00.000.019.2719.28+0.01 LaplacianScore00.000.0−18.40−18.40+0.00 MI00.000.023.3223.31−0.01 MarkovBlanket00.000.021.0821.10+0.02 RFImportance00.000.025.9925.99+0.00 Random † 00.000.0– Total2,5019.4 1,7976.8– † RANDOM selects features at random by construction, so it is not counted as a fallback. LaplacianScore Random LOCO SBE* GainRatio (R)ReliefF mRMR SFS MarkovBlanket F-test MI Lasso ElasticNet CART RFImportance 900 950 1000 1050 1100 1150 1200 1250 1300 Elo 151412111109138765432151413121110987654321 with random fallbackno fallback Figure D.3: Impact of random fallback on Elo ranking. New Elo ratings are calculated based on the ablation study’s results for a single seed and are presented in orange. SBE is shown in striped red to indicate that excluding random fallback has a different effect on it than other methods. Default Elo ratings are presented in blue. Numbers on the bars represent rank. the time limit, resulting in a positive∆ = +18.05p. However, this reflects a budget violation rather than a real gain and is not comparable to the impact of the fallback on other methods. When SFS and LOCO time out, they output less features than the cardinality constraint assumes, resulting in a negative∆ =−18.95p and− 1.47p. Without fallback, SFS even falls below Random as its search iterations are relatively long (Table D.2). As in Guyon & Elisseeff (2003), we observe that randomly selected features still carry useful signal and improve downstream model’s performance compared to passing it an incomplete feature subset. For mRMR and methods that do resort to random fallback, ∆≤ 0.02p, which is attributable to run-to-run nondeterminism. 43 Figure D.3 presents the change in the Elo ranking when random fallback is removed. Without random fallback, SBE rises from rank 12 to rank 1 and SFS falls from rank 8 to rank 13, with both these shifts affecting all the other ranks as Elo rating is relative. We note, again, that SBE rise is an artifact of the random fallback’s impact on backward search methods and it reflects budget violation as described above. That is why the only meaningful change is the drop in the SFS rank. D.3STABILITY We assess the stability of the feature selection methods across datasets with a different number of samples (in the minority class) per feature, i.e., events per variable (EPV). We use the EPV as a proxy for the selection difficulty (Harrell, 2015; Heinze et al., 2018). F-Test and Distance-Based Methods Dominate Stability Rankings.The dominance of F-Test and LaplacianScore is visible in Figure 5 and Figure D.4, where we additionally highlight the top-3 performing methods. This confirms that F-Test, LaplacianScore, MI, GainRatio, (R)ReliefF, and mRMR consistently rank highest in stability across EPVs. In Figure D.5, we isolate the near-optimal set of methods that are within 3% of the best-performing method. This confirms the dominance of the aforementioned 5 methods. F-Test Works Well Across Selection Difficulties.The F-Test often performs well at high and medium EPVs. Importantly, it also ranks highest at very low EPVs, where other methods struggle. In this setting, other methods are rarely within its 3% threshold. Lower EPV Reveals Robustness.Comparing the different selection difficulties, one can observe a broader diversity of methods achieving competitive stability at high and medium EPVs. Conversely, at very low EPVs, only few methods are relatively stable. Most Methods Show Intermediate to Good Stability.In Table D.4, we report the mean stability for each method. Poor stability is defined by values below 0.40, intermediate to good stability by values between 0.40 and 0.75, and values above 0.75 indicate excellent stability (Nogueira & Brown, 2016). Lasso, SBE, SFS, and LOCO only achieve low stability, showing the impact of the high number of timeouts for SBE (46.41%), SFS (45.28%), and LOCO (23.10%). The remaining methods show an intermediate to good stability. 44 0.00.23.415.1217.42799.7 Events per Variable F-Test LaplacianScore MI GainRatio (R)ReliefF mRMR RFImportance ElasticNet MarkovBlanket CART Lasso SFS SBE LOCO Random 0.0 0.2 0.4 0.6 0.8 1.0 unstable stable Figure D.4: Stability. Nogueira score (Nogueira & Brown, 2016) across datasets. We use the events per variable, i.e., the number of samples per feature for regression problems or the number of samples in the minority class per feature for classification problems, as a proxy for the selection difficulty (Harrell, 2015; Heinze et al., 2018). The top-3 methods per EPV are highlighted. 0.00.23.415.1217.42799.7 Events per Variable F-Test LaplacianScore MI GainRatio (R)ReliefF mRMR RFImportance ElasticNet MarkovBlanket CART Lasso SFS SBE LOCO Random Within Threshold Outside Threshold Figure D.5: Thresholded stability. We display all methods within a 3% performance threshold of the best-performing method. 45 D.4VALIDITY Like the stability, we assess the validity across different events per variable (EPV) (Appendix D.3). Additionally, we use three different noise levels relative to the original feature set size: a 50% noise level indicates that a number of noise features equal to 50% of the original feature count has been injected into the dataset. This proportional scaling applies consistently to the 75% and 100% noise levels. The 50% noise level results in one third of the features to be noise, the 100% noise level results in half of the feature to be noise. An increasing noise level represents a higher difficulty for feature selection methods to make a valid selection, because the amount of irrelevant features increases. One common strategy is to use feature removal approaches like RemOve And Retrain (ROAR), but these methods can be sensitive to feature redundancies that are prevalent in real-world data (Hooker et al., 2019). Therefore, we adopt an alternative approach to measure validity described in Chapter 3.3. F-Test and Information-Theoretic Methods Dominate Validity Rankings.In Figure 6, we can see that F-Test and information-theoretic methods dominate the validity rankings, followed by LaplacianScore. This finding is consistent across different noise levels (Figures D.6, D.7, D.8, D.9, D.10, D.11). More Noise Injection Reveals Robustness.In contrast to the stability, we can observe a higher resilience to lower EPVs for the validity. However, Figures D.6, D.8, and D.10 illustrate the challenge of increasing noise levels, as visualized by the progressively lighter coloring across all methods. This degradation pattern is quantified in Table D.4, where every method exhibits a consistent score decline with increasing noise levels. Dominant Methods Show Greater Noise Resilience.The F-Test’s validity score decreases steadily from 0.92 and 0.89 to 0.85 — a modest drop of<4% per noise increment. In contrast, MarkovBlanket experiences substantially larger declines of 9% per step, indicating the superior noise robustness of top-performing methods. In fact, the method ranking only changes slightly at different noise levels (Figures D.6, D.7, D.8, D.9, D.10, D.11). More Noise Injection Accentuates Differences.We can observe that with increasing noise levels, the number of low-ranked methods achieving a performance within a threshold of 3% of the best method decreases (Figures D.7, D.9, D.11). This implies that the increasing difficulty emphasizes differences between high- and low-ranking methods. Table D.4: Mean validity and stability. We report the average scores across all datasets. Higher is better. MethodStabilityValidity (50%)Validity (75%)Validity (100%) F-Test0.6790270.9224440.8855310.848069 LaplacianScore0.6563190.8754470.8374050.797492 MI0.6538490.8792160.8317290.785338 GainRatio0.6483430.9095950.8678290.826548 (R)ReliefF0.6026950.8369290.7755360.740539 mRMR0.5862070.7499560.6986820.654502 RFImportance0.5434650.7699100.7182010.672429 ElasticNet0.4422660.8535440.8044360.759485 MarkovBlanket0.4374990.6218390.5322420.460249 CART0.4272320.7085250.6507550.588315 Lasso0.3742020.8397660.7781330.731577 SFS0.2764850.7814490.7028610.626375 SBE0.2350750.7264020.6345860.558420 LOCO0.1727240.7303260.6399940.583660 Random0.0005980.6751380.5868240.499406 46 0.00.10.76.340.02799.7 Events per Variable F-Test GainRatio MI LaplacianScore ElasticNet Lasso (R)ReliefF SFS RFImportance mRMR LOCO SBE CART Random MarkovBlanket 0.0 0.2 0.4 0.6 0.8 1.0 invalid valid Figure D.6: Validity at 50% added noise. Selection precision across datasets at a noise level of 50%. Like for the stability, we use the events per variable, i.e., the number of samples per feature for regression problems or the number of samples in the minority class per feature for classification problems, as a proxy for the selection difficulty (Harrell, 2015; Heinze et al., 2018). The top-3 methods per EPV are highlighted. 0.00.10.76.340.02799.7 Events per Variable F-Test GainRatio MI LaplacianScore ElasticNet Lasso (R)ReliefF SFS RFImportance mRMR LOCO SBE CART Random MarkovBlanket Within Threshold Outside Threshold Figure D.7: Thresholded validity at 50% added noise. We display all methods within a 3% performance threshold of the best-performing method at a noise level of 50%. 47 0.00.10.76.340.02799.7 Events per Variable F-Test GainRatio LaplacianScore MI ElasticNet Lasso (R)ReliefF RFImportance SFS mRMR CART LOCO SBE Random MarkovBlanket 0.0 0.2 0.4 0.6 0.8 1.0 invalid valid Figure D.8: Validity at 75% added noise. Selection precision across datasets at a noise level of 75%. Like for the stability, we use the events per variable, i.e., the number of samples per feature for regression problems or the number of samples in the minority class per feature for classification problems, as a proxy for the selection difficulty (Harrell, 2015; Heinze et al., 2018). The top-3 methods per EPV are highlighted. 0.00.10.76.340.02799.7 Events per Variable F-Test GainRatio LaplacianScore MI ElasticNet Lasso (R)ReliefF RFImportance SFS mRMR CART LOCO SBE Random MarkovBlanket Within Threshold Outside Threshold Figure D.9: Thresholded validity at 75% added noise. We display all methods within a 3% performance threshold of the best-performing method at a noise level of 75%. 48 0.00.10.76.340.02799.7 Events per Variable F-Test GainRatio LaplacianScore MI ElasticNet (R)ReliefF Lasso RFImportance mRMR SFS CART LOCO SBE Random MarkovBlanket 0.0 0.2 0.4 0.6 0.8 1.0 invalid valid Figure D.10: Validity at 100% added noise. Selection precision across datasets at a noise level of 100%. Like for the stability, we use the events per variable, i.e., the number of samples per feature for regression problems or the number of samples in the minority class per feature for classification problems, as a proxy for the selection difficulty (Harrell, 2015; Heinze et al., 2018). The top-3 methods per EPV are highlighted. 0.00.10.76.340.02799.7 Events per Variable F-Test GainRatio LaplacianScore MI ElasticNet (R)ReliefF Lasso RFImportance mRMR SFS CART LOCO SBE Random MarkovBlanket Within Threshold Outside Threshold Figure D.11: Thresholded validity at 100% added noise. We display all methods within a 3% performance threshold of the best-performing method at a noise level of 100%. 49 D.5RAW PREDICTIVE PERFORMANCE In the following, we show the mean and standard deviation of the metric error per method across datasets and budgets aggregated over three seeds. We break down the tables per downstream model for better legibility. Table D.5 shows the performance for LGBM, Table D.6 for Linear model, Table D.7 for RF, Table D.8 for RealMLP, and Table D.9 for TabICLv2. Table D.5: Per-cell results with downstream model LGBM. Tasks are grouped by metric. Each cell is the mean over three seeds, with the sample standard deviation as a subscript. DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCO MarkovBlanket MImRMR(R)ReliefFRandomSBERFImportanceSFS log-loss (↓) horse_colic survival 1 .778 ±.020 .762 ±.037 .759 ±.028 .834 ±.065 .879 ±.027 .762 ±.037 .784 ±.075 .779 ±.031 .823 ±.066 .807 ±.021 .809 ±.042 .863 ±.073 .821 ±.081 .760 ±.037 .894 ±.012 2 .753 ±.046 .739 ±.043 .748 ±.031 .805 ±.034 .889 ±.029 .738 ±.036 .763 ±.051 .761 ±.042 .796 ±.063 .790 ±.026 .767 ±.045 .802 ±.042 .774 ±.043 .739 ±.059 .887 ±.011 3 .746 ±.037 .742 ±.033 .728 ±.049 .773 ±.013 .886 ±.022 .737 ±.038 .760 ±.054 .743 ±.043 .777 ±.038 .764 ±.015 .749 ±.035 .776 ±.083 .748 ±.049 .727 ±.057 .886 ±.016 4 .730 ±.046 .719 ±.043 .723 ±.045 .731 ±.048 .793 ±.008 .720 ±.046 .757 ±.051 .730 ±.054 .746 ±.036 .732 ±.035 .743 ±.037 .759 ±.048 .737 ±.050 .718 ±.056 .852 ±.016 5 .725 ±.042 .725 ±.035 .719 ±.043 .725 ±.042 .752 ±.021 .722 ±.039 .759 ±.044 .731 ±.041 .743 ±.034 .716 ±.050 .735 ±.038 .751 ±.039 .737 ±.050 .725 ±.047 .770 ±.030 srbct prediction 1 .698 ±.234 .292 ±.099 .521 ±.176 .556 ±.306 1.36 ±.09 .368 ±.237 1.19 ±.10 .635 ±.176 .550 ±.219 .532 ±.256 .366 ±.102 1.00 ±.37 1.09 ±.38 .405 ±.166 1.16 ±.22 2 .512 ±.184 .147 ±.091 .198 ±.085 .269 ±.180 1.06 ±.08 .149 ±.139 .799 ±.127 .207 ±.106 .229 ±.210 .211 ±.106 .103 ±.031 1.13 ±.21 .969 ±.158 .0764 ±.0675 .933 ±.316 3 .367 ±.118 .113 ±.104 .106 ±.111 .125 ±.090 .900 ±.093 .0943 ±.0808 .544 ±.183 .125 ±.094 .136 ±.087 .123 ±.095 .0935 ±.0853 .458 ±.194 .536 ±.215 .104 ±.056 .505 ±.158 4 .224 ±.156 .119 ±.093 .115 ±.096 .115 ±.069 .519 ±.164 .0980 ±.0736 .219 ±.071 .121 ±.077 .129 ±.087 .113 ±.073 .0931 ±.0818 .340 ±.061 .390 ±.084 .0954 ±.0653 .288 ±.295 5 .183 ±.108 .128 ±.082 .116 ±.081 .114 ±.069 .0963 ±.0578 .106 ±.070 .133 ±.022 .118 ±.064 .128 ±.081 .115 ±.070 .110 ±.066 .124 ±.013 .145 ±.016 .104 ±.063 .251 ±.147 sdss_17 1 .0968 ±.0004 .113 ±.015 .141 ±.001 .142 ±.003 .719 ±.000 .242 ±.252 .0973 ±.0011 .0973 ±.0011 .146 ±.001 .157 ±.002 .159 ±.003 .555 ±.409 .0968 ±.0004 .142 ±.003 .146 ±.001 2 .0740 ±.0010 .0762 ±.0032 .0942 ±.0106 .0754 ±.0009 .528 ±.142 .0746 ±.0018 .0739 ±.0010 .0774 ±.0063 .0829 ±.0010 .153 ±.003 .0987 ±.0031 .102 ±.008 .0741 ±.0011 .0751 ±.0011 .0983 ±.0014 3 .0795 ±.0012 .0718 ±.0009 .0869 ±.0145 .0795 ±.0012 .439 ±.023 .0718 ±.0009 .0722 ±.0007 .0795 ±.0012 .0795 ±.0012 .0939 ±.0014 .0978 ±.0115 .390 ±.038 .0722 ±.0008 .0768 ±.0051 .0809 ±.0011 4 .0787 ±.0011 .0717 ±.0007 .0866 ±.0028 .0787 ±.0012 .188 ±.146 .0760 ±.0079 .0743 ±.0049 .0787 ±.0012 .0845 ±.0011 .0912 ±.0013 .0845 ±.0012 .307 ±.181 .0720 ±.0008 .0787 ±.0012 .0788 ±.0011 5 .0841 ±.0011 .0835 ±.0013 .0837 ±.0016 .0841 ±.0011 .0844 ±.0056 .0834 ±.0013 .0786 ±.0012 .0823 ±.0040 .0841 ±.0011 .0845 ±.0011 .0843 ±.0011 .187 ±.189 .0786 ±.0012 .0841 ±.0011 .0786 ±.0012 predict_students_dropout and_academic_success 1 .634 ±.035 .595 ±.018 .652 ±.013 .672 ±.007 .975 ±.017 .600 ±.019 .617 ±.036 .603 ±.007 .640 ±.010 .682 ±.023 .614 ±.016 .693 ±.034 .871 ±.029 .674 ±.009 .663 ±.010 2 .579 ±.016 .574 ±.018 .576 ±.018 .602 ±.006 .882 ±.031 .574 ±.021 .583 ±.013 .594 ±.012 .570 ±.016 .633 ±.015 .582 ±.012 .640 ±.023 .640 ±.053 .581 ±.012 .615 ±.028 3 .565 ±.014 .560 ±.018 .570 ±.027 .596 ±.012 .680 ±.030 .562 ±.013 .564 ±.021 .596 ±.006 .559 ±.017 .602 ±.013 .565 ±.024 .660 ±.019 .599 ±.045 .567 ±.015 .581 ±.015 4 .560 ±.017 .558 ±.017 .562 ±.018 .590 ±.005 .599 ±.014 .558 ±.012 .556 ±.018 .591 ±.005 .561 ±.015 .596 ±.014 .560 ±.024 .594 ±.016 .558 ±.015 .555 ±.014 .562 ±.017 5 .553 ±.015 .554 ±.016 .554 ±.019 .575 ±.027 .554 ±.016 .555 ±.016 .557 ±.018 .587 ±.008 .556 ±.014 .564 ±.016 .555 ±.017 .559 ±.024 .552 ±.013 .553 ±.016 .556 ±.013 ancestry study 1 1.74 ±.04 1.81 ±.05 1.82 ±.05 2.04 ±.14 2.12 ±.01 1.91 ±.17 1.81 ±.09 1.76 ±.06 1.75 ±.04 1.76 ±.03 1.78 ±.06 2.04 ±.12 2.08 ±.03 1.75 ±.05 1.83 ±.06 2 1.75 ±.03 1.78 ±.07 1.79 ±.08 1.85 ±.14 2.12 ±.01 1.92 ±.17 1.79 ±.07 1.78 ±.05 1.71 ±.01 1.76 ±.03 1.78 ±.06 2.11 ±.01 2.10 ±.04 1.77 ±.03 1.85 ±.08 3 1.74 ±.05 1.81 ±.08 1.79 ±.06 1.80 ±.03 2.11 ±.01 1.78 ±.07 1.81 ±.05 1.78 ±.05 1.70 ±.06 1.77 ±.05 1.79 ±.10 2.11 ±.01 2.06 ±.10 1.77 ±.05 1.82 ±.01 4 1.77 ±.03 1.78 ±.07 1.81 ±.07 1.76 ±.03 2.03 ±.01 1.78 ±.08 1.76 ±.05 1.78 ±.02 1.71 ±.02 1.77 ±.03 1.80 ±.09 1.95 ±.19 1.96 ±.05 1.71 ±.03 1.87 ±.03 5 1.70 ±.03 1.82 ±.05 1.71 ±.01 1.80 ±.03 1.79 ±.06 1.76 ±.08 1.73 ±.02 1.79 ±.03 1.72 ±.01 1.79 ±.02 1.79 ±.06 1.99 ±.14 1.78 ±.12 1.75 ±.01 1.85 ±.02 otto_group_product classification_challenge 1 .711 ±.005 .783 ±.025 .909 ±.016 1.18 ±.00 .909 ±.024 .856 ±.004 .966 ±.086 .719 ±.007 .925 ±.015 .825 ±.005 .940 ±.033 1.12 ±.11 1.11 ±.08 .718 ±.006 1.12 ±.15 2 .561 ±.008 .591 ±.010 .660 ±.014 .862 ±.007 .649 ±.016 .612 ±.017 .735 ±.036 .575 ±.006 .646 ±.009 .637 ±.009 .638 ±.003 .833 ±.014 .800 ±.026 .568 ±.010 .807 ±.085 3 .508 ±.006 .523 ±.005 .559 ±.007 .604 ±.005 .532 ±.008 .527 ±.002 .670 ±.073 .517 ±.012 .573 ±.006 .560 ±.007 .553 ±.008 .645 ±.021 .636 ±.033 .520 ±.011 .644 ±.034 4 .486 ±.007 .492 ±.005 .512 ±.003 .540 ±.005 .500 ±.005 .493 ±.007 .584 ±.017 .495 ±.007 .509 ±.006 .507 ±.007 .511 ±.006 .570 ±.018 .571 ±.035 .492 ±.008 .583 ±.037 5 .479 ±.007 .482 ±.008 .486 ±.009 .491 ±.008 .484 ±.007 .482 ±.009 .511 ±.013 .481 ±.007 .487 ±.009 .486 ±.005 .486 ±.009 .510 ±.022 .528 ±.011 .480 ±.009 .520 ±.020 lung_cancer 1 .438 ±.245 .438 ±.080 .527 ±.102 .290 ±.131 .868 ±.065 .501 ±.121 .716 ±.060 .354 ±.059 .418 ±.022 .377 ±.185 .379 ±.010 .754 ±.095 .739 ±.054 .339 ±.106 .688 ±.123 2 .390 ±.206 .241 ±.081 .374 ±.063 .266 ±.080 .851 ±.064 .337 ±.135 .413 ±.093 .263 ±.086 .265 ±.100 .227 ±.037 .284 ±.053 .507 ±.131 .404 ±.059 .200 ±.073 .427 ±.138 3 .288 ±.070 .212 ±.091 .265 ±.041 .250 ±.041 .719 ±.136 .220 ±.057 .301 ±.002 .248 ±.071 .227 ±.011 .232 ±.026 .219 ±.043 .257 ±.056 .261 ±.061 .192 ±.044 .274 ±.066 4 .238 ±.030 .209 ±.050 .215 ±.041 .203 ±.029 .590 ±.059 .203 ±.033 .254 ±.088 .223 ±.038 .204 ±.020 .199 ±.048 .246 ±.046 .197 ±.078 .207 ±.073 .209 ±.014 .198 ±.054 5 .237 ±.008 .194 ±.046 .203 ±.030 .198 ±.030 .271 ±.113 .212 ±.022 .207 ±.013 .193 ±.032 .204 ±.033 .202 ±.043 .206 ±.029 .196 ±.026 .189 ±.023 .208 ±.016 .185 ±.011 soybean large 1 1.29 ±.14 1.97 ±.05 1.83 ±.21 1.98 ±.32 1.86 ±.25 1.74 ±.19 1.15 ±.22 .971 ±.089 1.60 ±.32 1.16 ±.12 1.68 ±.05 2.04 ±.18 1.12 ±.22 1.13 ±.28 .788 ±.207 2 .758 ±.057 1.25 ±.07 1.02 ±.09 .888 ±.249 1.01 ±.44 1.34 ±.19 .834 ±.055 .662 ±.083 .961 ±.094 .994 ±.020 1.40 ±.20 1.11 ±.20 .889 ±.249 .707 ±.017 .627 ±.139 3 .623 ±.061 .890 ±.112 .810 ±.180 .495 ±.036 .690 ±.223 1.04 ±.25 .747 ±.102 .566 ±.082 .472 ±.078 .704 ±.023 .885 ±.202 .642 ±.066 .478 ±.122 .593 ±.091 .537 ±.113 4 .520 ±.100 .621 ±.055 .623 ±.204 .441 ±.141 .543 ±.173 .680 ±.103 .633 ±.031 .453 ±.145 .462 ±.093 .537 ±.048 .682 ±.168 .534 ±.017 .477 ±.146 .458 ±.117 .462 ±.171 5 .495 ±.125 .477 ±.121 .508 ±.107 .450 ±.165 .453 ±.155 .600 ±.052 .522 ±.090 .448 ±.168 .479 ±.117 .450 ±.140 .503 ±.097 .500 ±.121 .480 ±.143 .441 ±.163 .449 ±.145 mic 1 .551 ±.001 .574 ±.007 .541 ±.024 .556 ±.009 .695 ±.014 .546 ±.046 .635 ±.066 .520 ±.023 .553 ±.016 .562 ±.024 .554 ±.012 .698 ±.007 .696 ±.006 .548 ±.029 .534 ±.019 2 .546 ±.018 .551 ±.020 .531 ±.006 .558 ±.008 .691 ±.013 .549 ±.023 .624 ±.062 .517 ±.024 .555 ±.009 .539 ±.025 .567 ±.009 .690 ±.008 .661 ±.025 .527 ±.006 .544 ±.026 3 .542 ±.011 .531 ±.019 .543 ±.013 .536 ±.022 .685 ±.019 .525 ±.012 .581 ±.053 .537 ±.028 .524 ±.002 .522 ±.011 .573 ±.010 .584 ±.020 .618 ±.048 .525 ±.024 .549 ±.037 4 .524 ±.020 .520 ±.017 .538 ±.006 .524 ±.006 .667 ±.015 .520 ±.020 .550 ±.028 .522 ±.016 .518 ±.033 .528 ±.009 .550 ±.002 .562 ±.079 .540 ±.027 .524 ±.018 .537 ±.029 5 .511 ±.022 .512 ±.015 .507 ±.016 .520 ±.008 .634 ±.012 .515 ±.020 .501 ±.016 .517 ±.002 .499 ±.025 .519 ±.015 .516 ±.023 .537 ±.029 .527 ±.015 .506 ±.018 .516 ±.027 ljubljana primary_tumor 1 1.77 ±.14 1.91 ±.13 1.95 ±.09 1.77 ±.11 2.13 ±.06 1.90 ±.13 1.84 ±.13 1.88 ±.15 1.77 ±.14 1.83 ±.13 1.95 ±.09 1.94 ±.07 1.81 ±.14 1.83 ±.13 1.83 ±.12 2 1.67 ±.18 1.79 ±.21 1.73 ±.12 1.64 ±.22 1.97 ±.08 1.79 ±.19 1.80 ±.17 1.69 ±.21 1.55 ±.19 1.64 ±.22 1.71 ±.14 1.88 ±.18 1.75 ±.20 1.70 ±.15 1.78 ±.21 3 1.50 ±.22 1.70 ±.19 1.61 ±.12 1.57 ±.20 1.86 ±.09 1.67 ±.13 1.58 ±.13 1.56 ±.16 1.47 ±.21 1.57 ±.20 1.64 ±.12 1.70 ±.19 1.65 ±.17 1.50 ±.20 1.63 ±.23 4 1.43 ±.19 1.62 ±.16 1.49 ±.15 1.51 ±.24 1.69 ±.12 1.55 ±.20 1.46 ±.15 1.49 ±.21 1.44 ±.19 1.51 ±.24 1.55 ±.14 1.57 ±.06 1.46 ±.14 1.41 ±.17 1.51 ±.18 5 1.41 ±.17 1.50 ±.18 1.47 ±.13 1.46 ±.19 1.54 ±.14 1.45 ±.17 1.41 ±.15 1.46 ±.21 1.41 ±.18 1.46 ±.19 1.50 ±.18 1.49 ±.24 1.43 ±.17 1.41 ±.17 1.43 ±.16 anneal 1 .0709 ±.0278 .131 ±.056 .242 ±.020 .104 ±.019 .772 ±.072 .132 ±.041 .117 ±.011 .123 ±.033 .142 ±.008 .123 ±.042 .135 ±.017 .616 ±.101 .102 ±.045 .109 ±.048 .0900 ±.0279 2 .0753 ±.0257 .0751 ±.0429 .107 ±.066 .0911 ±.0235 .607 ±.034 .0633 ±.0400 .0853 ±.0448 .0944 ±.0200 .0947 ±.0397 .0907 ±.0228 .0736 ±.0134 .253 ±.111 .0911 ±.0343 .0743 ±.0429 .0674 ±.0309 3 .0823 ±.0237 .0737 ±.0388 .0559 ±.0169 .0933 ±.0244 .381 ±.064 .0765 ±.0393 .0833 ±.0421 .0818 ±.0339 .0826 ±.0360 .0933 ±.0244 .0784 ±.0420 .149 ±.059 .0881 ±.0266 .0767 ±.0434 .0667 ±.0311 4 .0817 ±.0311 .0752 ±.0377 .0622 ±.0311 .100 ±.036 .250 ±.039 .0756 ±.0386 .0843 ±.0423 .0796 ±.0428 .0796 ±.0420 .100 ±.036 .0651 ±.0358 .173 ±.103 .0871 ±.0269 .0746 ±.0425 .0670 ±.0285 5 .0770 ±.0452 .0769 ±.0444 .0783 ±.0387 .0940 ±.0341 .0481 ±.0065 .0751 ±.0425 .0837 ±.0421 .0804 ±.0424 .0788 ±.0434 .0940 ±.0341 .0722 ±.0425 .0767 ±.0413 .0867 ±.0273 .0745 ±.0424 .0646 ±.0271 50 (continued from previous page) DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCO MarkovBlanket MImRMR(R)ReliefFRandomSBERFImportanceSFS audiology diagnosis 1 .452 ±.160 .521 ±.018 .504 ±.019 .472 ±.138 .565 ±.067 .540 ±.006 .560 ±.067 .472 ±.138 .435 ±.144 .551 ±.049 .475 ±.039 .585 ±.064 .511 ±.096 .417 ±.159 .471 ±.134 2 .447 ±.165 .518 ±.015 .472 ±.037 .470 ±.134 .483 ±.078 .523 ±.018 .560 ±.067 .463 ±.148 .417 ±.163 .520 ±.074 .445 ±.050 .551 ±.134 .499 ±.080 .416 ±.155 .460 ±.117 3 .440 ±.153 .489 ±.040 .439 ±.050 .470 ±.133 .436 ±.102 .495 ±.030 .551 ±.075 .454 ±.154 .420 ±.161 .448 ±.101 .408 ±.107 .431 ±.094 .517 ±.104 .416 ±.155 .460 ±.117 4 .432 ±.139 .438 ±.088 .408 ±.143 .444 ±.131 .440 ±.105 .416 ±.102 .551 ±.075 .418 ±.162 .420 ±.161 .424 ±.126 .409 ±.117 .397 ±.126 .517 ±.104 .416 ±.155 .464 ±.123 5 .432 ±.139 .432 ±.099 .408 ±.143 .416 ±.154 .437 ±.105 .402 ±.122 .548 ±.070 .416 ±.154 .416 ±.155 .417 ±.135 .399 ±.125 .425 ±.144 .506 ±.089 .416 ±.155 .466 ±.127 RMSE (↓) qsar_tid_11 1 1.34 ±.02 1.42 ±.03 1.39 ±.03 1.53 ±.02 1.40 ±.03 1.44 ±.03 1.46 ±.05 1.53 ±.01 1.36 ±.05 1.53 ±.02 1.49 ±.01 1.53 ±.01 1.53 ±.01 1.33 ±.02 1.37 ±.02 2 1.23 ±.01 1.27 ±.04 1.31 ±.03 1.50 ±.01 1.32 ±.00 1.26 ±.04 1.36 ±.06 1.47 ±.05 1.25 ±.02 1.50 ±.01 1.34 ±.06 1.50 ±.02 1.50 ±.04 1.21 ±.01 1.33 ±.04 3 1.00 ±.02 1.05 ±.01 1.05 ±.02 1.37 ±.03 1.17 ±.02 1.03 ±.02 1.17 ±.02 1.33 ±.04 1.02 ±.01 1.37 ±.03 1.04 ±.02 1.30 ±.03 1.29 ±.03 .977 ±.028 1.26 ±.04 4 .820 ±.003 .844 ±.030 .844 ±.015 1.03 ±.05 .941 ±.003 .838 ±.022 .962 ±.021 1.01 ±.03 .845 ±.010 1.03 ±.05 .815 ±.011 .976 ±.058 1.01 ±.08 .801 ±.021 .990 ±.026 5 .769 ±.014 .777 ±.023 .773 ±.014 .806 ±.033 .779 ±.002 .778 ±.021 .814 ±.023 .807 ±.034 .767 ±.015 .806 ±.033 .753 ±.022 .802 ±.027 .794 ±.024 .758 ±.013 .802 ±.032 forest fires 1 1.41 ±.04 1.40 ±.02 1.44 ±.05 1.42 ±.05 1.40 ±.03 1.40 ±.02 1.40 ±.03 1.42 ±.03 1.41 ±.03 1.41 ±.03 1.43 ±.06 1.40 ±.03 1.40 ±.03 1.42 ±.04 1.40 ±.03 2 1.41 ±.02 1.41 ±.03 1.43 ±.05 1.41 ±.03 1.41 ±.04 1.41 ±.03 1.43 ±.04 1.41 ±.03 1.42 ±.05 1.42 ±.03 1.41 ±.04 1.41 ±.02 1.42 ±.05 1.42 ±.04 1.41 ±.03 3 1.41 ±.03 1.42 ±.03 1.42 ±.03 1.41 ±.03 1.41 ±.04 1.42 ±.03 1.42 ±.03 1.40 ±.02 1.41 ±.04 1.42 ±.04 1.42 ±.04 1.42 ±.03 1.41 ±.02 1.41 ±.03 1.42 ±.05 4 1.41 ±.03 1.41 ±.02 1.41 ±.02 1.41 ±.03 1.41 ±.03 1.41 ±.02 1.41 ±.02 1.42 ±.04 1.41 ±.03 1.42 ±.03 1.42 ±.04 1.42 ±.04 1.41 ±.03 1.41 ±.03 1.42 ±.05 5 1.41 ±.03 1.41 ±.02 1.41 ±.03 1.41 ±.02 1.41 ±.03 1.41 ±.02 1.41 ±.03 1.41 ±.02 1.41 ±.02 1.41 ±.02 1.41 ±.03 1.41 ±.03 1.41 ±.03 1.41 ±.02 1.41 ±.03 allstate claims_severity 1 .667 ±.001 .680 ±.002 .675 ±.002 .705 ±.010 .811 ±.002 .680 ±.002 .669 ±.001 .693 ±.000 .670 ±.002 .716 ±.013 .695 ±.003 .783 ±.024 .785 ±.023 .667 ±.001 .661 ±.001 2 .637 ±.003 .651 ±.002 .658 ±.001 .648 ±.003 .808 ±.003 .651 ±.002 .633 ±.006 .637 ±.001 .646 ±.001 .660 ±.014 .650 ±.012 .784 ±.016 .793 ±.021 .639 ±.004 .644 ±.005 3 .587 ±.001 .603 ±.004 .642 ±.001 .600 ±.011 .807 ±.002 .603 ±.004 .604 ±.007 .613 ±.002 .616 ±.002 .615 ±.001 .636 ±.013 .729 ±.022 .734 ±.012 .587 ±.001 .631 ±.018 4 .558 ±.002 .562 ±.004 .612 ±.008 .569 ±.001 .778 ±.009 .562 ±.004 .564 ±.006 .613 ±.001 .565 ±.002 .602 ±.002 .593 ±.011 .669 ±.033 .672 ±.030 .558 ±.001 .605 ±.022 5 .545 ±.001 .541 ±.001 .555 ±.004 .562 ±.001 .681 ±.011 .541 ±.001 .544 ±.003 .555 ±.001 .547 ±.001 .581 ±.008 .568 ±.009 .598 ±.017 .608 ±.018 .545 ±.001 .578 ±.005 miami housing 1 .248 ±.016 .261 ±.003 .323 ±.001 .272 ±.006 .330 ±.003 .261 ±.003 .340 ±.046 .194 ±.004 .203 ±.025 .313 ±.003 .367 ±.001 .451 ±.083 .198 ±.019 .254 ±.022 .261 ±.005 2 .179 ±.005 .223 ±.004 .235 ±.004 .175 ±.002 .230 ±.001 .223 ±.004 .219 ±.038 .185 ±.002 .187 ±.001 .181 ±.007 .242 ±.023 .303 ±.094 .161 ±.009 .183 ±.002 .171 ±.006 3 .152 ±.007 .172 ±.008 .171 ±.002 .170 ±.001 .225 ±.001 .172 ±.002 .156 ±.007 .176 ±.005 .170 ±.004 .168 ±.003 .178 ±.023 .206 ±.023 .147 ±.004 .153 ±.007 .146 ±.004 4 .149 ±.005 .159 ±.002 .145 ±.003 .163 ±.002 .226 ±.002 .158 ±.002 .144 ±.003 .163 ±.005 .163 ±.002 .153 ±.011 .146 ±.003 .181 ±.029 .145 ±.003 .146 ±.003 .145 ±.003 5 .144 ±.003 .143 ±.003 .143 ±.002 .144 ±.003 .197 ±.001 .143 ±.003 .143 ±.002 .144 ±.003 .144 ±.003 .144 ±.003 .144 ±.003 .160 ±.014 .142 ±.003 .144 ±.003 .142 ±.003 wine quality 1 .736 ±.008 .762 ±.018 .763 ±.007 .763 ±.007 .815 ±.009 .762 ±.018 .825 ±.012 .736 ±.008 .763 ±.007 .775 ±.006 .755 ±.016 .785 ±.032 .755 ±.017 .736 ±.008 .775 ±.006 2 .692 ±.005 .704 ±.007 .713 ±.009 .719 ±.010 .741 ±.012 .704 ±.007 .769 ±.009 .711 ±.009 .726 ±.013 .728 ±.013 .713 ±.007 .756 ±.036 .696 ±.014 .692 ±.008 .707 ±.003 3 .663 ±.004 .692 ±.006 .697 ±.010 .675 ±.010 .683 ±.007 .692 ±.006 .677 ±.020 .676 ±.013 .680 ±.019 .686 ±.018 .669 ±.011 .700 ±.022 .668 ±.012 .660 ±.006 .677 ±.008 4 .643 ±.011 .661 ±.005 .662 ±.010 .648 ±.011 .665 ±.007 .661 ±.005 .651 ±.012 .645 ±.010 .649 ±.016 .657 ±.008 .643 ±.008 .670 ±.010 .644 ±.009 .642 ±.012 .656 ±.008 5 .633 ±.010 .638 ±.011 .645 ±.007 .633 ±.008 .643 ±.009 .638 ±.011 .635 ±.006 .636 ±.008 .634 ±.018 .644 ±.011 .630 ±.010 .640 ±.008 .631 ±.011 .630 ±.012 .639 ±.007 body_density prediction 1 .0117 ±.0005 .0114 ±.0011 .0117 ±.0011 .0117 ±.0011 .0128 ±.0030 .0114 ±.0011 .0129 ±.0025 .0116 ±.0011 .0119 ±.0012 .0119 ±.0013 .0116 ±.0012 .0153 ±.0042 .0118 ±.0013 .0115 ±.0009 .0114 ±.0011 2 .0114 ±.0009 .0113 ±.0011 .0117 ±.0011 .0118 ±.0011 .0117 ±.0009 .0113 ±.0011 .0131 ±.0027 .0117 ±.0011 .0117 ±.0010 .0116 ±.0009 .0115 ±.0010 .0138 ±.0025 .0115 ±.0013 .0113 ±.0010 .0115 ±.0012 3 .0113 ±.0010 .0114 ±.0011 .0119 ±.0009 .0117 ±.0011 .0118 ±.0013 .0114 ±.0011 .0116 ±.0015 .0117 ±.0012 .0119 ±.0009 .0118 ±.0010 .0115 ±.0011 .0123 ±.0021 .0115 ±.0011 .0115 ±.0012 .0115 ±.0012 4 .0114 ±.0011 .0114 ±.0010 .0118 ±.0009 .0119 ±.0010 .0118 ±.0013 .0114 ±.0010 .0115 ±.0011 .0119 ±.0009 .0120 ±.0009 .0117 ±.0010 .0115 ±.0011 .0124 ±.0007 .0114 ±.0012 .0114 ±.0011 .0114 ±.0012 5 .0114 ±.0010 .0113 ±.0010 .0116 ±.0011 .0118 ±.0012 .0115 ±.0010 .0113 ±.0010 .0115 ±.0010 .0119 ±.0013 .0119 ±.0013 .0115 ±.0009 .0114 ±.0010 .0130 ±.0008 .0115 ±.0012 .0115 ±.0011 .0115 ±.0012 brasilian houses ( × 10 3 ) 1 12.6 ±6.2 14.1 ±7.1 12.6 ±6.2 13.4 ±6.8 15.2 ±6.1 14.1 ±7.1 14.9 ±6.0 12.6 ±6.2 13.4 ±6.8 12.6 ±6.2 14.2 ±6.8 15.4 ±5.3 12.6 ±6.4 12.6 ±6.2 15.3 ±3.2 2 13.7 ±6.8 13.7 ±6.8 13.7 ±6.8 13.8 ±6.8 15.3 ±6.2 13.7 ±6.8 13.4 ±5.2 13.5 ±6.5 13.7 ±6.8 13.7 ±6.8 14.1 ±7.1 13.9 ±6.7 13.3 ±5.3 13.7 ±6.8 14.5 ±5.7 3 12.3 ±6.0 14.0 ±7.0 14.0 ±7.0 14.0 ±6.9 14.3 ±6.6 13.6 ±6.5 12.7 ±6.1 12.3 ±5.9 13.2 ±6.4 14.0 ±7.1 13.7 ±6.7 14.0 ±7.0 12.4 ±6.2 12.3 ±5.9 14.5 ±6.1 4 12.5 ±5.8 13.7 ±6.7 12.0 ±5.4 12.0 ±5.4 14.2 ±6.7 13.6 ±6.6 12.7 ±6.2 11.4 ±5.2 12.0 ±5.4 12.7 ±6.0 13.7 ±6.7 13.7 ±6.8 12.6 ±6.3 12.5 ±5.8 13.7 ±6.8 5 11.2 ±5.1 14.0 ±7.0 12.6 ±6.0 12.6 ±5.9 13.7 ±6.8 13.9 ±6.9 12.6 ±6.1 12.6 ±5.9 12.6 ±5.9 12.6 ±5.9 12.6 ±5.9 12.7 ±6.0 11.9 ±6.0 12.6 ±5.9 14.0 ±7.1 automobile 1 .738 ±.091 .817 ±.126 .732 ±.159 .802 ±.099 1.18 ±.09 .794 ±.158 .720 ±.044 1.17 ±.04 .731 ±.160 .893 ±.034 .814 ±.119 .937 ±.195 .791 ±.065 .719 ±.102 .798 ±.101 2 .685 ±.050 .761 ±.194 .783 ±.199 .828 ±.068 1.02 ±.11 .761 ±.194 .680 ±.059 .982 ±.154 .787 ±.142 .857 ±.069 .752 ±.191 .839 ±.063 .658 ±.089 .646 ±.128 .772 ±.133 3 .644 ±.108 .786 ±.187 .784 ±.190 .842 ±.132 .804 ±.012 .786 ±.187 .665 ±.076 .669 ±.081 .732 ±.206 .792 ±.122 .736 ±.198 .841 ±.234 .642 ±.084 .655 ±.113 .754 ±.139 4 .653 ±.100 .760 ±.204 .728 ±.205 .762 ±.167 .766 ±.006 .760 ±.204 .665 ±.076 .658 ±.133 .647 ±.100 .693 ±.138 .760 ±.151 .661 ±.069 .648 ±.079 .641 ±.098 .741 ±.157 5 .641 ±.107 .675 ±.093 .672 ±.080 .745 ±.183 .679 ±.089 .675 ±.093 .669 ±.070 .638 ±.104 .636 ±.101 .671 ±.132 .664 ±.066 .735 ±.041 .654 ±.077 .638 ±.102 .687 ±.048 pva_revenue prediction_kddcup98 1 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 2 .598 ±.002 .597 ±.002 .597 ±.001 .598 ±.002 .598 ±.002 .597 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 3 .598 ±.002 .597 ±.002 .596 ±.002 .598 ±.002 .598 ±.002 .596 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .597 ±.002 4 .598 ±.002 .596 ±.002 .596 ±.002 .598 ±.002 .598 ±.002 .596 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .597 ±.002 .597 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .597 ±.002 5 .598 ±.002 .596 ±.002 .596 ±.002 .597 ±.002 .598 ±.002 .596 ±.002 .597 ±.002 .598 ±.002 .597 ±.001 .597 ±.002 .597 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.001 superconductivity 1 9.75 ±.18 9.62 ±.02 10.0 ±.1 9.72 ±.03 10.5 ±.2 9.83 ±.04 9.71 ±.08 9.66 ±.13 10.7 ±.4 9.74 ±.11 9.82 ±.11 9.75 ±.09 9.81 ±.07 9.71 ±.15 9.63 ±.11 2 9.53 ±.07 9.48 ±.05 9.54 ±.06 9.47 ±.07 9.51 ±.05 9.52 ±.05 9.48 ±.05 9.50 ±.10 9.48 ±.06 9.50 ±.14 9.54 ±.06 9.61 ±.08 9.56 ±.06 9.46 ±.05 9.50 ±.01 3 9.41 ±.05 9.48 ±.03 9.47 ±.03 9.42 ±.05 9.42 ±.05 9.44 ±.04 9.36 ±.05 9.43 ±.10 9.38 ±.03 9.38 ±.11 9.43 ±.03 9.45 ±.03 9.41 ±.05 9.42 ±.05 9.39 ±.05 4 9.35 ±.04 9.38 ±.05 9.40 ±.02 9.37 ±.04 9.36 ±.02 9.36 ±.02 9.37 ±.06 9.38 ±.03 9.32 ±.07 9.37 ±.06 9.36 ±.03 9.38 ±.05 9.45 ±.10 9.35 ±.04 9.35 ±.02 5 9.32 ±.06 9.33 ±.04 9.34 ±.03 9.32 ±.03 9.31 ±.04 9.32 ±.07 9.31 ±.05 9.34 ±.05 9.29 ±.02 9.33 ±.03 9.33 ±.04 9.33 ±.02 9.33 ±.07 9.33 ±.06 9.36 ±.05 AUC (↑) in_vehicle_coupon recommendation 1 .739 ±.014 .719 ±.006 .697 ±.008 .718 ±.012 .584 ±.017 .720 ±.006 .737 ±.009 .727 ±.010 .722 ±.023 .715 ±.016 .737 ±.012 .606 ±.038 .734 ±.015 .742 ±.009 .742 ±.009 2 .789 ±.008 .724 ±.008 .724 ±.007 .763 ±.012 .623 ±.005 .725 ±.009 .797 ±.023 .765 ±.010 .762 ±.011 .737 ±.011 .778 ±.016 .663 ±.019 .804 ±.010 .794 ±.008 .761 ±.018 3 .802 ±.003 .768 ±.004 .738 ±.005 .800 ±.004 .675 ±.002 .768 ±.004 .815 ±.007 .802 ±.007 .803 ±.011 .762 ±.010 .801 ±.004 .735 ±.047 .826 ±.010 .800 ±.007 .798 ±.038 4 .826 ±.006 .797 ±.009 .809 ±.021 .817 ±.006 .732 ±.003 .798 ±.010 .823 ±.006 .815 ±.005 .823 ±.007 .800 ±.006 .816 ±.011 .809 ±.009 .833 ±.008 .832 ±.007 .818 ±.024 5 .833 ±.005 .827 ±.002 .825 ±.007 .828 ±.003 .811 ±.006 .827 ±.002 .834 ±.005 .824 ±.012 .830 ±.010 .824 ±.004 .830 ±.006 .826 ±.003 .833 ±.005 .832 ±.004 .829 ±.008 marketing campaign 1 .815 ±.042 .818 ±.038 .769 ±.016 .780 ±.022 .709 ±.020 .817 ±.038 .787 ±.027 .801 ±.011 .761 ±.023 .808 ±.043 .736 ±.013 .753 ±.034 .828 ±.029 .824 ±.025 .793 ±.048 2 .850 ±.024 .869 ±.027 .871 ±.022 .822 ±.026 .808 ±.016 .874 ±.032 .858 ±.008 .849 ±.011 .824 ±.024 .853 ±.037 .814 ±.038 .770 ±.036 .874 ±.024 .848 ±.030 .850 ±.010 3 .875 ±.020 .889 ±.022 .872 ±.019 .870 ±.011 .858 ±.017 .891 ±.020 .883 ±.021 .868 ±.009 .859 ±.018 .865 ±.024 .873 ±.026 .829 ±.019 .885 ±.022 .865 ±.023 .887 ±.030 4 .888 ±.020 .893 ±.024 .881 ±.017 .876 ±.012 .856 ±.019 .897 ±.027 .895 ±.025 .877 ±.008 .889 ±.012 .884 ±.024 .885 ±.024 .865 ±.040 .897 ±.019 .886 ±.019 .900 ±.025 5 .895 ±.017 .900 ±.018 .899 ±.016 .884 ±.017 .876 ±.037 .898 ±.019 .897 ±.025 .896 ±.010 .898 ±.014 .893 ±.021 .890 ±.018 .882 ±.036 .898 ±.020 .895 ±.019 .900 ±.022 51 (continued from previous page) DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCO MarkovBlanket MImRMR(R)ReliefFRandomSBERFImportanceSFS polish_companies bankruptcy 1 .949 ±.016 .827 ±.005 .863 ±.022 .902 ±.025 .833 ±.017 .822 ±.014 .936 ±.008 .936 ±.016 .922 ±.019 .928 ±.015 .844 ±.006 .842 ±.009 .875 ±.009 .947 ±.013 .933 ±.017 2 .951 ±.014 .855 ±.032 .878 ±.022 .931 ±.022 .858 ±.012 .859 ±.030 .944 ±.014 .943 ±.014 .936 ±.016 .938 ±.012 .928 ±.027 .888 ±.057 .920 ±.040 .952 ±.020 .940 ±.019 3 .952 ±.016 .897 ±.042 .879 ±.022 .941 ±.017 .863 ±.011 .900 ±.038 .943 ±.017 .942 ±.014 .938 ±.016 .946 ±.015 .933 ±.022 .889 ±.051 .937 ±.033 .953 ±.018 .943 ±.019 4 .954 ±.012 .907 ±.032 .913 ±.037 .940 ±.017 .873 ±.014 .907 ±.032 .945 ±.012 .949 ±.014 .942 ±.014 .952 ±.015 .940 ±.019 .943 ±.005 .955 ±.013 .955 ±.016 .949 ±.015 5 .954 ±.014 .933 ±.026 .940 ±.017 .945 ±.014 .884 ±.004 .934 ±.026 .947 ±.013 .950 ±.015 .952 ±.013 .954 ±.015 .946 ±.011 .943 ±.008 .954 ±.014 .954 ±.015 .955 ±.009 colon_tumor 1 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 2 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 3 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 4 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 5 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 mechanisms of_action 1 .943 ±.005 .939 ±.010 .940 ±.009 .939 ±.011 .933 ±.007 .942 ±.016 .907 ±.027 .935 ±.002 .939 ±.009 .938 ±.006 .887 ±.054 .916 ±.015 .934 ±.019 .936 ±.013 .939 ±.009 2 .941 ±.009 .946 ±.010 .935 ±.010 .938 ±.008 .941 ±.011 .945 ±.010 .936 ±.004 .939 ±.010 .939 ±.012 .945 ±.010 .945 ±.008 .933 ±.004 .936 ±.004 .941 ±.005 .937 ±.011 3 .942 ±.005 .943 ±.016 .947 ±.014 .940 ±.010 .941 ±.011 .940 ±.010 .940 ±.010 .944 ±.013 .944 ±.011 .946 ±.008 .941 ±.009 .942 ±.012 .943 ±.008 .936 ±.010 .943 ±.009 4 .948 ±.006 .940 ±.006 .943 ±.012 .946 ±.008 .946 ±.008 .942 ±.012 .945 ±.017 .944 ±.011 .942 ±.011 .947 ±.016 .943 ±.011 .948 ±.010 .941 ±.007 .945 ±.020 .944 ±.007 5 .942 ±.009 .943 ±.007 .945 ±.008 .946 ±.004 .943 ±.006 .943 ±.004 .945 ±.017 .946 ±.008 .945 ±.006 .944 ±.005 .946 ±.008 .943 ±.010 .947 ±.004 .946 ±.009 .944 ±.013 santander_customer transaction_prediction 1 .629 ±.007 .630 ±.006 .632 ±.004 .632 ±.004 .501 ±.001 .630 ±.006 .587 ±.020 .632 ±.004 .502 ±.002 .632 ±.004 .503 ±.008 .510 ±.005 .530 ±.007 .632 ±.004 .575 ±.011 2 .697 ±.002 .700 ±.002 .702 ±.003 .701 ±.003 .501 ±.001 .700 ±.002 .631 ±.021 .701 ±.004 .508 ±.002 .701 ±.003 .505 ±.002 .574 ±.027 .578 ±.032 .700 ±.001 .578 ±.007 3 .783 ±.005 .786 ±.004 .786 ±.005 .781 ±.004 .515 ±.002 .786 ±.004 .637 ±.021 .784 ±.003 .522 ±.002 .787 ±.003 .509 ±.003 .602 ±.033 .598 ±.016 .785 ±.003 .612 ±.021 4 .860 ±.004 .868 ±.003 .867 ±.003 .863 ±.005 .525 ±.002 .868 ±.003 .677 ±.010 .859 ±.003 .545 ±.004 .861 ±.004 .525 ±.004 .651 ±.012 .637 ±.018 .866 ±.004 .671 ±.023 5 .896 ±.003 .896 ±.003 .897 ±.003 .897 ±.003 .543 ±.007 .896 ±.003 .779 ±.013 .891 ±.005 .684 ±.003 .888 ±.002 .565 ±.028 .771 ±.019 .764 ±.010 .895 ±.003 .780 ±.014 nci_prostate cancer 1 .948 ±.023 .937 ±.003 .927 ±.010 .917 ±.029 .543 ±.026 .909 ±.050 .746 ±.087 .912 ±.056 .923 ±.015 .949 ±.015 .877 ±.058 .786 ±.077 .746 ±.112 .933 ±.020 .757 ±.054 2 .961 ±.012 .960 ±.019 .953 ±.028 .969 ±.014 .685 ±.089 .981 ±.011 .880 ±.051 .977 ±.006 .972 ±.005 .973 ±.007 .952 ±.020 .857 ±.062 .863 ±.033 .956 ±.024 .875 ±.041 3 .966 ±.007 .983 ±.002 .968 ±.010 .978 ±.009 .799 ±.075 .987 ±.002 .940 ±.011 .983 ±.008 .991 ±.005 .984 ±.003 .975 ±.014 .957 ±.007 .949 ±.017 .991 ±.002 .946 ±.025 4 .983 ±.011 .990 ±.006 .982 ±.008 .988 ±.001 .917 ±.038 .989 ±.001 .952 ±.016 .990 ±.005 .990 ±.002 .986 ±.003 .980 ±.012 .974 ±.012 .972 ±.008 .989 ±.002 .969 ±.015 5 .986 ±.009 .989 ±.004 .989 ±.003 .989 ±.003 .956 ±.014 .991 ±.003 .991 ±.009 .989 ±.004 .989 ±.004 .986 ±.004 .989 ±.006 .979 ±.009 .983 ±.006 .990 ±.003 .985 ±.004 bank_customer churn 1 .741 ±.010 .767 ±.012 .767 ±.012 .816 ±.003 .597 ±.011 .767 ±.012 .816 ±.003 .816 ±.003 .816 ±.003 .816 ±.003 .749 ±.060 .540 ±.060 .816 ±.003 .741 ±.010 .816 ±.003 2 .758 ±.008 .784 ±.006 .784 ±.006 .848 ±.007 .766 ±.003 .784 ±.006 .846 ±.007 .848 ±.007 .832 ±.011 .832 ±.007 .814 ±.004 .683 ±.068 .831 ±.012 .758 ±.008 .836 ±.004 3 .830 ±.001 .799 ±.004 .833 ±.026 .847 ±.008 .784 ±.008 .799 ±.004 .855 ±.015 .847 ±.008 .855 ±.014 .832 ±.007 .829 ±.003 .708 ±.092 .841 ±.003 .830 ±.001 .843 ±.005 4 .844 ±.002 .843 ±.032 .864 ±.005 .847 ±.008 .813 ±.011 .843 ±.032 .862 ±.004 .847 ±.008 .859 ±.014 .844 ±.003 .842 ±.006 .798 ±.052 .864 ±.006 .842 ±.002 .858 ±.014 5 .864 ±.005 .842 ±.033 .864 ±.006 .853 ±.011 .813 ±.012 .842 ±.033 .864 ±.006 .859 ±.003 .864 ±.006 .846 ±.002 .851 ±.008 .837 ±.034 .864 ±.006 .863 ±.005 .864 ±.005 hepatitis_survival prediction 1 .775 ±.083 .776 ±.072 .800 ±.013 .807 ±.046 .779 ±.091 .800 ±.044 .769 ±.029 .832 ±.031 .780 ±.023 .833 ±.032 .813 ±.067 .816 ±.043 .791 ±.087 .762 ±.024 .745 ±.098 2 .800 ±.013 .821 ±.063 .833 ±.029 .822 ±.004 .811 ±.038 .821 ±.033 .752 ±.035 .844 ±.022 .811 ±.038 .834 ±.009 .838 ±.035 .776 ±.025 .797 ±.093 .794 ±.050 .744 ±.068 3 .817 ±.030 .804 ±.035 .811 ±.021 .813 ±.027 .825 ±.043 .806 ±.031 .766 ±.051 .828 ±.042 .823 ±.020 .812 ±.024 .795 ±.059 .803 ±.008 .815 ±.070 .816 ±.034 .754 ±.074 4 .812 ±.015 .796 ±.033 .813 ±.031 .802 ±.033 .845 ±.011 .806 ±.022 .793 ±.060 .810 ±.034 .815 ±.028 .816 ±.023 .810 ±.048 .814 ±.041 .837 ±.037 .812 ±.024 .797 ±.050 5 .817 ±.010 .805 ±.027 .804 ±.023 .808 ±.025 .813 ±.012 .805 ±.022 .799 ±.046 .812 ±.025 .807 ±.025 .808 ±.031 .824 ±.037 .811 ±.044 .808 ±.015 .809 ±.023 .814 ±.066 customer_satisfaction in_airline 1 .957 ±.000 .920 ±.001 .935 ±.002 .924 ±.000 .940 ±.001 .920 ±.001 .941 ±.002 .961 ±.000 .959 ±.000 .913 ±.000 .956 ±.000 .858 ±.052 .968 ±.002 .959 ±.000 .971 ±.001 2 .979 ±.000 .977 ±.001 .956 ±.001 .976 ±.000 .979 ±.000 .977 ±.001 .976 ±.000 .977 ±.000 .977 ±.000 .973 ±.000 .976 ±.005 .888 ±.019 .985 ±.001 .974 ±.001 .984 ±.001 3 .989 ±.001 .989 ±.001 .981 ±.008 .985 ±.000 .989 ±.000 .989 ±.001 .990 ±.001 .987 ±.001 .986 ±.000 .983 ±.000 .989 ±.000 .977 ±.011 .992 ±.000 .985 ±.001 .991 ±.001 4 .992 ±.000 .990 ±.000 .990 ±.001 .989 ±.000 .992 ±.000 .990 ±.000 .993 ±.000 .989 ±.000 .990 ±.000 .990 ±.000 .992 ±.000 .990 ±.003 .993 ±.000 .990 ±.001 .993 ±.000 5 .994 ±.000 .993 ±.000 .993 ±.000 .992 ±.000 .993 ±.000 .993 ±.000 .994 ±.000 .992 ±.000 .993 ±.001 .993 ±.000 .994 ±.000 .993 ±.001 .994 ±.000 .994 ±.000 .994 ±.000 heloc 1 .772 ±.008 .765 ±.007 .771 ±.008 .768 ±.012 .665 ±.003 .768 ±.005 .756 ±.048 .784 ±.010 .769 ±.012 .772 ±.014 .729 ±.014 .723 ±.051 .781 ±.009 .772 ±.010 .765 ±.008 2 .776 ±.008 .777 ±.007 .784 ±.012 .784 ±.009 .768 ±.009 .780 ±.006 .782 ±.017 .789 ±.010 .787 ±.011 .789 ±.009 .790 ±.007 .752 ±.018 .789 ±.010 .780 ±.009 .787 ±.008 3 .792 ±.007 .785 ±.008 .793 ±.007 .793 ±.005 .768 ±.011 .787 ±.006 .786 ±.017 .793 ±.007 .794 ±.006 .792 ±.008 .794 ±.007 .772 ±.008 .792 ±.009 .792 ±.007 .790 ±.010 4 .794 ±.008 .794 ±.007 .794 ±.007 .794 ±.007 .778 ±.007 .794 ±.007 .793 ±.009 .794 ±.007 .794 ±.008 .794 ±.008 .794 ±.007 .784 ±.010 .793 ±.009 .795 ±.007 .793 ±.009 5 .795 ±.008 .795 ±.008 .795 ±.008 .795 ±.007 .791 ±.012 .794 ±.008 .794 ±.009 .795 ±.008 .795 ±.007 .794 ±.008 .795 ±.008 .792 ±.005 .793 ±.009 .795 ±.008 .793 ±.009 hiva agnostic 1 .577 ±.035 .657 ±.054 .562 ±.030 .528 ±.057 .522 ±.014 .580 ±.034 .543 ±.098 .506 ±.032 .604 ±.078 .528 ±.057 .606 ±.068 .589 ±.029 .574 ±.042 .577 ±.064 .683 ±.060 2 .670 ±.030 .706 ±.039 .624 ±.079 .651 ±.016 .557 ±.025 .727 ±.032 .651 ±.023 .625 ±.043 .615 ±.086 .651 ±.016 .661 ±.075 .630 ±.040 .596 ±.073 .696 ±.055 .673 ±.075 3 .706 ±.059 .776 ±.032 .674 ±.090 .697 ±.042 .604 ±.061 .755 ±.019 .740 ±.050 .724 ±.052 .717 ±.073 .697 ±.042 .730 ±.032 .732 ±.047 .714 ±.056 .774 ±.031 .738 ±.038 4 .755 ±.023 .774 ±.022 .783 ±.038 .730 ±.003 .698 ±.044 .759 ±.022 .727 ±.041 .776 ±.027 .764 ±.054 .730 ±.003 .779 ±.010 .775 ±.021 .780 ±.019 .789 ±.031 .759 ±.023 5 .780 ±.011 .793 ±.025 .793 ±.013 .771 ±.024 .785 ±.007 .777 ±.023 .755 ±.028 .788 ±.013 .777 ±.022 .771 ±.024 .774 ±.035 .791 ±.022 .800 ±.009 .790 ±.030 .785 ±.021 bank marketing 1 .677 ±.013 .711 ±.010 .711 ±.010 .683 ±.014 .624 ±.007 .711 ±.010 .702 ±.008 .698 ±.007 .674 ±.013 .668 ±.007 .676 ±.008 .673 ±.021 .708 ±.006 .666 ±.007 .714 ±.009 2 .717 ±.009 .730 ±.010 .729 ±.008 .725 ±.012 .650 ±.007 .730 ±.010 .733 ±.011 .733 ±.009 .732 ±.013 .696 ±.007 .700 ±.009 .704 ±.026 .730 ±.005 .714 ±.013 .732 ±.011 3 .726 ±.011 .741 ±.009 .741 ±.010 .737 ±.010 .734 ±.011 .741 ±.009 .744 ±.015 .739 ±.010 .750 ±.009 .733 ±.006 .717 ±.011 .737 ±.015 .746 ±.016 .726 ±.011 .737 ±.008 4 .746 ±.017 .757 ±.010 .753 ±.011 .748 ±.011 .747 ±.010 .755 ±.012 .759 ±.011 .748 ±.011 .759 ±.010 .753 ±.011 .746 ±.009 .749 ±.006 .755 ±.016 .731 ±.010 .747 ±.012 5 .762 ±.010 .761 ±.013 .759 ±.010 .753 ±.008 .759 ±.010 .762 ±.011 .761 ±.012 .760 ±.013 .761 ±.011 .759 ±.010 .759 ±.010 .754 ±.011 .760 ±.011 .762 ±.010 .755 ±.010 leukemia allaml 1 .887 ±.031 .982 ±.016 .997 ±.005 .990 ±.012 .728 ±.049 .948 ±.045 .646 ±.130 .985 ±.000 .990 ±.009 .980 ±.018 .982 ±.025 .714 ±.310 .746 ±.268 .990 ±.009 .554 ±.065 2 .909 ±.059 .998 ±.004 .993 ±.013 .997 ±.005 .836 ±.151 .992 ±.007 .743 ±.062 .952 ±.049 .997 ±.005 .995 ±.009 1.00 ±.00 .634 ±.132 .792 ±.153 .997 ±.005 .741 ±.131 3 .960 ±.036 .997 ±.005 .988 ±.015 .997 ±.005 .878 ±.152 .997 ±.005 .923 ±.036 .980 ±.034 .997 ±.005 .997 ±.005 .997 ±.005 .936 ±.054 .950 ±.033 1.00 ±.00 .939 ±.020 4 .972 ±.037 .997 ±.005 .997 ±.005 .997 ±.005 .974 ±.033 .997 ±.005 .975 ±.011 .990 ±.011 .997 ±.005 .997 ±.005 .997 ±.005 .957 ±.027 .954 ±.040 .997 ±.005 .982 ±.009 5 .977 ±.020 .997 ±.005 .997 ±.005 .997 ±.005 1.00 ±.00 .995 ±.009 .992 ±.014 1.00 ±.00 .997 ±.005 .997 ±.005 .997 ±.005 .974 ±.025 .990 ±.012 .997 ±.005 .997 ±.005 heart_disease cleveland 1 .772 ±.032 .818 ±.018 .794 ±.064 .842 ±.061 .756 ±.020 .814 ±.015 .859 ±.090 .817 ±.027 .798 ±.040 .789 ±.017 .787 ±.053 .818 ±.038 .840 ±.045 .777 ±.024 .830 ±.037 2 .829 ±.073 .848 ±.007 .848 ±.004 .870 ±.055 .796 ±.016 .855 ±.043 .862 ±.076 .828 ±.015 .856 ±.024 .831 ±.001 .866 ±.037 .832 ±.029 .833 ±.071 .867 ±.053 .828 ±.044 3 .860 ±.038 .882 ±.023 .864 ±.008 .879 ±.052 .818 ±.026 .875 ±.034 .865 ±.040 .838 ±.024 .877 ±.055 .851 ±.011 .888 ±.025 .819 ±.028 .880 ±.040 .872 ±.053 .859 ±.056 4 .873 ±.033 .881 ±.027 .888 ±.014 .880 ±.055 .855 ±.040 .877 ±.032 .875 ±.051 .880 ±.055 .876 ±.053 .889 ±.034 .884 ±.017 .893 ±.039 .872 ±.032 .879 ±.040 .883 ±.051 5 .886 ±.033 .895 ±.029 .897 ±.030 .882 ±.042 .883 ±.028 .885 ±.040 .888 ±.043 .892 ±.055 .884 ±.063 .884 ±.063 .889 ±.018 .872 ±.063 .889 ±.043 .894 ±.041 .884 ±.038 clock_protein period 1 .568 ±.104 .626 ±.136 .569 ±.065 .504 ±.008 .631 ±.075 .626 ±.136 .573 ±.142 .561 ±.128 .491 ±.073 .496 ±.008 .526 ±.120 .651 ±.200 .649 ±.168 .503 ±.077 .610 ±.040 2 .635 ±.051 .684 ±.093 .617 ±.160 .538 ±.065 .614 ±.063 .684 ±.093 .519 ±.035 .543 ±.090 .582 ±.104 .532 ±.094 .586 ±.114 .507 ±.083 .520 ±.165 .598 ±.166 .638 ±.061 3 .683 ±.076 .646 ±.116 .647 ±.146 .578 ±.109 .698 ±.106 .646 ±.116 .557 ±.037 .593 ±.052 .631 ±.051 .578 ±.145 .538 ±.158 .674 ±.110 .690 ±.143 .603 ±.173 .656 ±.074 4 .665 ±.057 .670 ±.048 .646 ±.197 .619 ±.147 .725 ±.104 .670 ±.048 .554 ±.032 .600 ±.122 .584 ±.105 .578 ±.149 .621 ±.186 .598 ±.050 .624 ±.146 .612 ±.127 .637 ±.056 5 .543 ±.069 .543 ±.067 .651 ±.171 .644 ±.174 .633 ±.054 .543 ±.067 .593 ±.170 .668 ±.158 .635 ±.147 .635 ±.147 .631 ±.171 .651 ±.122 .665 ±.141 .638 ±.140 .601 ±.069 52 (continued from previous page) DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCO MarkovBlanket MImRMR(R)ReliefFRandomSBERFImportanceSFS churn 1 .828 ±.011 .875 ±.005 .866 ±.009 .812 ±.012 .690 ±.008 .769 ±.203 .717 ±.011 .821 ±.007 .866 ±.009 .858 ±.034 .794 ±.011 .677 ±.125 .891 ±.003 .866 ±.010 .891 ±.003 2 .905 ±.023 .903 ±.001 .904 ±.001 .841 ±.015 .732 ±.081 .852 ±.088 .810 ±.084 .859 ±.011 .881 ±.011 .892 ±.012 .815 ±.012 .714 ±.101 .911 ±.008 .901 ±.002 .908 ±.001 3 .921 ±.003 .910 ±.002 .912 ±.013 .878 ±.008 .825 ±.010 .917 ±.006 .841 ±.062 .857 ±.009 .900 ±.013 .915 ±.007 .903 ±.001 .754 ±.122 .919 ±.003 .913 ±.006 .911 ±.007 4 .920 ±.005 .920 ±.004 .920 ±.005 .871 ±.009 .821 ±.013 .920 ±.003 .877 ±.063 .861 ±.002 .908 ±.009 .918 ±.006 .910 ±.008 .847 ±.028 .916 ±.005 .919 ±.003 .915 ±.003 5 .913 ±.004 .919 ±.003 .915 ±.003 .867 ±.002 .910 ±.003 .918 ±.003 .915 ±.004 .860 ±.001 .911 ±.008 .914 ±.003 .909 ±.006 .880 ±.056 .914 ±.004 .918 ±.002 .916 ±.002 aps_failure 1 .980 ±.006 .958 ±.041 .977 ±.005 .963 ±.014 .928 ±.014 .977 ±.001 .949 ±.024 .980 ±.005 .982 ±.004 .972 ±.005 .942 ±.003 .966 ±.017 .942 ±.016 .984 ±.004 .976 ±.007 2 .987 ±.003 .981 ±.011 .981 ±.004 .977 ±.004 .960 ±.007 .984 ±.003 .977 ±.008 .983 ±.004 .983 ±.003 .985 ±.002 .967 ±.001 .979 ±.008 .973 ±.003 .987 ±.003 .982 ±.006 3 .989 ±.003 .990 ±.002 .985 ±.003 .979 ±.005 .981 ±.003 .988 ±.001 .987 ±.004 .988 ±.003 .985 ±.004 .987 ±.003 .984 ±.003 .987 ±.004 .986 ±.003 .990 ±.003 .987 ±.003 4 .991 ±.002 .991 ±.002 .989 ±.003 .990 ±.003 .988 ±.002 .991 ±.001 .990 ±.003 .990 ±.004 .987 ±.003 .990 ±.002 .988 ±.004 .989 ±.002 .989 ±.004 .991 ±.002 .989 ±.003 5 .992 ±.002 .991 ±.003 .991 ±.003 .991 ±.003 .991 ±.002 .991 ±.002 .991 ±.003 .991 ±.003 .991 ±.003 .992 ±.002 .991 ±.002 .992 ±.003 .992 ±.003 .991 ±.003 .991 ±.003 labor negotiations 1 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 2 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 3 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 4 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 5 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 .500 ±.000 kdd_cup_09 appetency 1 .655 ±.076 .522 ±.019 .762 ±.016 .746 ±.017 .539 ±.029 .522 ±.019 .799 ±.009 .765 ±.011 .620 ±.027 .750 ±.020 .653 ±.021 .515 ±.010 .505 ±.004 .748 ±.017 .798 ±.015 2 .709 ±.070 .542 ±.056 .769 ±.016 .745 ±.018 .558 ±.040 .542 ±.056 .804 ±.013 .795 ±.016 .625 ±.019 .762 ±.014 .644 ±.041 .549 ±.009 .530 ±.025 .756 ±.014 .800 ±.017 3 .782 ±.021 .578 ±.035 .774 ±.017 .786 ±.013 .558 ±.037 .578 ±.035 .801 ±.010 .793 ±.014 .709 ±.092 .764 ±.023 .767 ±.008 .602 ±.033 .623 ±.019 .770 ±.002 .793 ±.017 4 .795 ±.010 .606 ±.055 .782 ±.023 .794 ±.017 .602 ±.015 .606 ±.055 .798 ±.009 .785 ±.008 .771 ±.003 .784 ±.019 .787 ±.011 .714 ±.036 .683 ±.044 .772 ±.008 .782 ±.010 5 .793 ±.011 .719 ±.107 .792 ±.014 .796 ±.018 .713 ±.014 .719 ±.107 .809 ±.011 .780 ±.001 .772 ±.004 .786 ±.014 .796 ±.008 .724 ±.040 .726 ±.040 .796 ±.010 .787 ±.013 qsar_oral toxicity 1 .720 ±.016 .693 ±.042 .693 ±.025 .568 ±.062 .562 ±.016 .696 ±.035 .553 ±.051 .552 ±.043 .686 ±.021 .568 ±.062 .568 ±.022 .578 ±.014 .570 ±.010 .693 ±.028 .736 ±.013 2 .772 ±.007 .748 ±.033 .717 ±.034 .622 ±.027 .586 ±.007 .781 ±.023 .624 ±.035 .635 ±.035 .727 ±.020 .622 ±.027 .677 ±.009 .634 ±.045 .661 ±.044 .736 ±.043 .745 ±.022 3 .833 ±.016 .827 ±.029 .799 ±.036 .739 ±.028 .729 ±.008 .839 ±.019 .750 ±.022 .759 ±.032 .806 ±.017 .739 ±.028 .827 ±.013 .757 ±.018 .751 ±.004 .820 ±.037 .799 ±.038 4 .876 ±.016 .862 ±.028 .876 ±.021 .844 ±.014 .844 ±.022 .870 ±.026 .846 ±.020 .853 ±.032 .867 ±.013 .844 ±.014 .893 ±.021 .847 ±.020 .844 ±.018 .889 ±.027 .850 ±.028 5 .895 ±.026 .888 ±.027 .895 ±.024 .884 ±.020 .882 ±.023 .886 ±.026 .885 ±.013 .891 ±.026 .896 ±.014 .884 ±.020 .899 ±.024 .881 ±.028 .877 ±.021 .896 ±.029 .884 ±.024 taiwanese_bankruptcy prediction 1 .932 ±.011 .927 ±.003 .928 ±.006 .932 ±.011 .933 ±.009 .923 ±.012 .922 ±.017 .929 ±.001 .932 ±.007 .932 ±.006 .917 ±.009 .936 ±.009 .927 ±.003 .937 ±.003 .936 ±.001 2 .934 ±.007 .934 ±.002 .931 ±.004 .937 ±.011 .939 ±.010 .941 ±.004 .936 ±.015 .934 ±.003 .936 ±.008 .941 ±.010 .929 ±.003 .934 ±.008 .939 ±.009 .937 ±.013 .941 ±.004 3 .935 ±.009 .939 ±.008 .928 ±.002 .940 ±.007 .938 ±.009 .945 ±.011 .943 ±.010 .937 ±.004 .938 ±.009 .939 ±.011 .932 ±.005 .939 ±.005 .944 ±.011 .942 ±.011 .942 ±.012 4 .942 ±.010 .946 ±.009 .934 ±.007 .939 ±.010 .936 ±.011 .947 ±.008 .942 ±.011 .940 ±.009 .940 ±.008 .941 ±.006 .942 ±.008 .941 ±.010 .942 ±.007 .942 ±.013 .942 ±.007 5 .942 ±.010 .945 ±.013 .939 ±.006 .939 ±.012 .942 ±.011 .946 ±.011 .942 ±.010 .943 ±.010 .944 ±.008 .940 ±.011 .941 ±.011 .946 ±.007 .940 ±.014 .941 ±.013 .944 ±.007 thyroid discordant 1 .969 ±.014 .778 ±.214 .892 ±.078 .974 ±.007 .533 ±.001 .778 ±.214 .967 ±.017 .965 ±.015 .923 ±.026 .911 ±.037 .935 ±.016 .706 ±.221 .976 ±.006 .971 ±.012 .927 ±.074 2 .967 ±.016 .923 ±.020 .907 ±.071 .966 ±.015 .568 ±.006 .923 ±.020 .970 ±.018 .955 ±.021 .921 ±.027 .931 ±.027 .906 ±.068 .742 ±.173 .982 ±.005 .969 ±.015 .923 ±.083 3 .969 ±.018 .942 ±.030 .939 ±.048 .968 ±.017 .551 ±.037 .942 ±.030 .970 ±.014 .955 ±.021 .936 ±.035 .935 ±.026 .914 ±.092 .952 ±.024 .983 ±.003 .969 ±.015 .929 ±.075 4 .968 ±.017 .945 ±.015 .946 ±.036 .969 ±.018 .580 ±.035 .945 ±.015 .970 ±.014 .956 ±.022 .940 ±.009 .928 ±.035 .966 ±.016 .931 ±.074 .984 ±.003 .970 ±.016 .927 ±.074 5 .969 ±.018 .951 ±.025 .969 ±.016 .969 ±.018 .908 ±.090 .951 ±.025 .970 ±.014 .970 ±.016 .958 ±.020 .955 ±.033 .965 ±.016 .971 ±.017 .984 ±.003 .970 ±.016 .946 ±.065 coil_2000 insurance_policies 1 .739 ±.037 .711 ±.066 .743 ±.016 .744 ±.015 .534 ±.003 .663 ±.090 .735 ±.027 .737 ±.028 .733 ±.006 .717 ±.034 .645 ±.019 .698 ±.045 .675 ±.045 .736 ±.032 .746 ±.032 2 .751 ±.025 .736 ±.041 .747 ±.020 .743 ±.022 .563 ±.035 .722 ±.043 .743 ±.026 .738 ±.021 .742 ±.021 .726 ±.044 .645 ±.021 .744 ±.029 .727 ±.033 .741 ±.033 .759 ±.024 3 .752 ±.024 .738 ±.040 .749 ±.024 .744 ±.022 .643 ±.025 .726 ±.043 .747 ±.023 .742 ±.023 .748 ±.019 .742 ±.030 .700 ±.028 .717 ±.013 .700 ±.047 .741 ±.023 .752 ±.026 4 .752 ±.023 .738 ±.040 .752 ±.022 .748 ±.023 .652 ±.021 .734 ±.040 .752 ±.020 .749 ±.016 .750 ±.014 .753 ±.009 .728 ±.018 .754 ±.024 .740 ±.019 .753 ±.022 .752 ±.026 5 .754 ±.021 .750 ±.030 .753 ±.022 .752 ±.026 .651 ±.024 .751 ±.027 .755 ±.024 .752 ±.022 .753 ±.021 .759 ±.020 .728 ±.017 .750 ±.026 .741 ±.037 .752 ±.022 .755 ±.021 prostate cancer 1 .922 ±.077 .951 ±.051 .960 ±.042 .956 ±.064 .520 ±.070 .943 ±.044 .681 ±.092 .948 ±.081 .956 ±.049 .935 ±.081 .949 ±.060 .663 ±.144 .664 ±.159 .947 ±.029 .854 ±.110 2 .932 ±.070 .956 ±.047 .961 ±.051 .969 ±.043 .567 ±.156 .949 ±.054 .745 ±.095 .953 ±.070 .965 ±.054 .958 ±.060 .964 ±.057 .784 ±.154 .781 ±.162 .958 ±.036 .871 ±.040 3 .941 ±.043 .955 ±.058 .955 ±.066 .957 ±.071 .676 ±.041 .964 ±.062 .791 ±.095 .955 ±.078 .963 ±.064 .961 ±.068 .961 ±.059 .851 ±.015 .810 ±.064 .962 ±.052 .910 ±.108 4 .953 ±.035 .957 ±.060 .967 ±.058 .964 ±.062 .841 ±.023 .969 ±.054 .926 ±.061 .949 ±.076 .962 ±.066 .957 ±.071 .963 ±.064 .930 ±.083 .925 ±.062 .961 ±.056 .931 ±.081 5 .960 ±.042 .960 ±.070 .964 ±.062 .963 ±.064 .926 ±.041 .971 ±.050 .925 ±.073 .958 ±.072 .962 ±.066 .962 ±.066 .964 ±.062 .963 ±.061 .962 ±.054 .961 ±.059 .965 ±.057 credit approval 1 .895 ±.011 .915 ±.008 .915 ±.006 .834 ±.004 .642 ±.111 .909 ±.007 .901 ±.019 .838 ±.018 .911 ±.006 .826 ±.014 .880 ±.011 .777 ±.066 .908 ±.010 .909 ±.007 .911 ±.013 2 .915 ±.010 .914 ±.008 .926 ±.015 .854 ±.008 .757 ±.051 .912 ±.002 .916 ±.012 .847 ±.002 .914 ±.008 .862 ±.054 .895 ±.015 .844 ±.111 .916 ±.013 .912 ±.003 .915 ±.017 3 .920 ±.012 .918 ±.004 .920 ±.015 .867 ±.013 .899 ±.005 .923 ±.004 .919 ±.008 .863 ±.009 .918 ±.011 .894 ±.037 .917 ±.007 .862 ±.054 .919 ±.006 .929 ±.012 .907 ±.012 4 .927 ±.005 .927 ±.009 .930 ±.011 .886 ±.034 .916 ±.015 .931 ±.005 .927 ±.012 .869 ±.014 .930 ±.015 .923 ±.008 .926 ±.012 .878 ±.047 .925 ±.008 .928 ±.011 .921 ±.012 5 .928 ±.005 .930 ±.009 .932 ±.008 .929 ±.007 .920 ±.010 .929 ±.008 .925 ±.010 .915 ±.034 .929 ±.009 .930 ±.010 .930 ±.006 .899 ±.037 .926 ±.008 .930 ±.008 .922 ±.007 heart_disease hungary 1 .671 ±.127 .831 ±.101 .818 ±.092 .816 ±.095 .612 ±.165 .836 ±.063 .711 ±.194 .790 ±.086 .805 ±.078 .811 ±.096 .811 ±.150 .689 ±.163 .827 ±.045 .802 ±.089 .840 ±.036 2 .767 ±.045 .857 ±.076 .838 ±.067 .813 ±.089 .855 ±.053 .857 ±.064 .722 ±.200 .783 ±.067 .847 ±.093 .824 ±.048 .867 ±.064 .800 ±.147 .841 ±.051 .757 ±.038 .864 ±.050 3 .800 ±.008 .837 ±.083 .843 ±.074 .796 ±.089 .861 ±.062 .836 ±.064 .756 ±.151 .800 ±.079 .844 ±.072 .839 ±.054 .835 ±.059 .818 ±.033 .840 ±.050 .797 ±.079 .864 ±.050 4 .825 ±.027 .848 ±.075 .843 ±.076 .796 ±.074 .851 ±.071 .846 ±.074 .853 ±.077 .795 ±.074 .845 ±.074 .848 ±.074 .835 ±.073 .792 ±.036 .844 ±.050 .824 ±.061 .866 ±.052 5 .829 ±.061 .844 ±.073 .844 ±.075 .827 ±.057 .842 ±.074 .838 ±.074 .844 ±.072 .808 ±.079 .839 ±.067 .840 ±.068 .839 ±.072 .827 ±.078 .836 ±.064 .835 ±.065 .836 ±.064 give_me some_credit 1 .626 ±.002 .732 ±.001 .732 ±.005 .732 ±.005 .608 ±.021 .732 ±.001 .758 ±.002 .732 ±.005 .759 ±.003 .732 ±.005 .658 ±.009 .702 ±.035 .751 ±.012 .626 ±.002 .759 ±.003 2 .772 ±.003 .807 ±.012 .813 ±.003 .784 ±.002 .703 ±.012 .807 ±.012 .813 ±.013 .787 ±.003 .796 ±.003 .809 ±.008 .687 ±.003 .796 ±.019 .798 ±.023 .703 ±.007 .799 ±.003 3 .779 ±.004 .824 ±.004 .826 ±.003 .788 ±.002 .740 ±.040 .824 ±.004 .826 ±.009 .799 ±.005 .826 ±.003 .826 ±.003 .788 ±.002 .797 ±.039 .825 ±.006 .779 ±.004 .811 ±.003 4 .822 ±.012 .829 ±.004 .833 ±.002 .796 ±.004 .804 ±.023 .829 ±.004 .841 ±.003 .804 ±.004 .836 ±.003 .829 ±.004 .818 ±.020 .823 ±.012 .842 ±.005 .838 ±.010 .835 ±.004 5 .845 ±.004 .840 ±.003 .842 ±.004 .833 ±.009 .838 ±.013 .840 ±.003 .845 ±.004 .829 ±.003 .846 ±.004 .840 ±.003 .843 ±.005 .846 ±.004 .845 ±.004 .845 ±.004 .844 ±.007 clock_protein toxicity 1 .520 ±.100 .514 ±.126 .460 ±.028 .591 ±.106 .470 ±.058 .514 ±.126 .486 ±.062 .403 ±.089 .568 ±.176 .554 ±.102 .490 ±.043 .493 ±.126 .497 ±.059 .532 ±.114 .519 ±.044 2 .489 ±.049 .501 ±.052 .482 ±.041 .558 ±.125 .470 ±.034 .501 ±.052 .547 ±.020 .517 ±.036 .516 ±.163 .576 ±.132 .530 ±.106 .543 ±.151 .542 ±.053 .571 ±.033 .480 ±.077 3 .509 ±.069 .536 ±.036 .538 ±.041 .571 ±.137 .449 ±.136 .536 ±.036 .466 ±.055 .519 ±.057 .472 ±.127 .557 ±.087 .549 ±.045 .471 ±.088 .516 ±.067 .542 ±.063 .558 ±.121 4 .509 ±.058 .539 ±.036 .524 ±.073 .521 ±.097 .523 ±.077 .539 ±.036 .488 ±.054 .542 ±.059 .518 ±.054 .548 ±.110 .505 ±.024 .543 ±.136 .581 ±.094 .516 ±.033 .513 ±.060 5 .549 ±.111 .519 ±.071 .476 ±.019 .519 ±.080 .557 ±.094 .519 ±.071 .496 ±.076 .516 ±.070 .491 ±.052 .511 ±.078 .519 ±.066 .485 ±.078 .518 ±.078 .495 ±.049 .540 ±.104 online_shoppers_purchasing intention_dataset 1 .894 ±.001 .897 ±.004 .891 ±.007 .884 ±.010 .678 ±.012 .897 ±.004 .920 ±.004 .908 ±.011 .894 ±.008 .918 ±.004 .708 ±.032 .693 ±.020 .922 ±.005 .897 ±.004 .881 ±.010 2 .902 ±.007 .929 ±.003 .905 ±.006 .911 ±.021 .712 ±.012 .929 ±.003 .926 ±.004 .932 ±.004 .909 ±.019 .925 ±.004 .862 ±.109 .708 ±.035 .928 ±.003 .902 ±.007 .927 ±.004 3 .930 ±.003 .930 ±.002 .933 ±.004 .933 ±.004 .751 ±.003 .931 ±.002 .927 ±.003 .932 ±.004 .931 ±.003 .926 ±.006 .927 ±.007 .905 ±.009 .930 ±.002 .930 ±.004 .930 ±.003 4 .931 ±.003 .931 ±.002 .931 ±.004 .931 ±.004 .929 ±.005 .931 ±.002 .929 ±.003 .932 ±.003 .931 ±.004 .933 ±.004 .933 ±.004 .924 ±.009 .931 ±.002 .931 ±.004 .930 ±.003 5 .931 ±.004 .932 ±.004 .931 ±.005 .931 ±.004 .931 ±.004 .932 ±.004 .932 ±.003 .931 ±.004 .932 ±.004 .933 ±.004 .934 ±.004 .867 ±.083 .932 ±.003 .931 ±.004 .930 ±.004 53 (continued from previous page) DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCO MarkovBlanket MImRMR(R)ReliefFRandomSBERFImportanceSFS credit_card clients_default 1 .758 ±.005 .756 ±.008 .742 ±.003 .742 ±.003 .682 ±.003 .756 ±.008 .761 ±.005 .763 ±.007 .743 ±.004 .749 ±.004 .750 ±.003 .696 ±.030 .762 ±.002 .763 ±.004 .748 ±.011 2 .772 ±.003 .773 ±.010 .777 ±.006 .778 ±.007 .729 ±.004 .769 ±.009 .771 ±.009 .777 ±.005 .770 ±.007 .763 ±.004 .774 ±.005 .740 ±.041 .774 ±.005 .770 ±.007 .768 ±.007 3 .777 ±.006 .776 ±.009 .778 ±.006 .779 ±.006 .758 ±.020 .775 ±.008 .780 ±.005 .782 ±.008 .779 ±.003 .777 ±.005 .782 ±.005 .770 ±.021 .779 ±.007 .776 ±.005 .775 ±.002 4 .781 ±.005 .781 ±.004 .781 ±.005 .781 ±.003 .775 ±.004 .780 ±.004 .782 ±.003 .782 ±.006 .781 ±.005 .779 ±.005 .784 ±.004 .772 ±.019 .782 ±.007 .781 ±.005 .779 ±.003 5 .782 ±.004 .784 ±.004 .784 ±.005 .784 ±.004 .784 ±.005 .783 ±.004 .783 ±.004 .784 ±.006 .783 ±.005 .784 ±.005 .783 ±.004 .773 ±.023 .784 ±.005 .784 ±.005 .781 ±.003 bioresponse 1 .806 ±.012 .760 ±.007 .778 ±.003 .670 ±.012 .507 ±.004 .771 ±.004 .615 ±.057 .774 ±.029 .805 ±.007 .741 ±.014 .763 ±.004 .545 ±.024 .550 ±.027 .796 ±.016 .760 ±.022 2 .829 ±.018 .799 ±.017 .815 ±.016 .735 ±.013 .506 ±.007 .814 ±.006 .717 ±.040 .832 ±.025 .842 ±.008 .776 ±.024 .801 ±.006 .721 ±.038 .664 ±.076 .835 ±.017 .785 ±.014 3 .843 ±.016 .844 ±.011 .839 ±.013 .826 ±.014 .514 ±.001 .840 ±.013 .808 ±.017 .847 ±.020 .849 ±.010 .812 ±.012 .836 ±.007 .760 ±.039 .717 ±.007 .850 ±.009 .823 ±.019 4 .857 ±.010 .860 ±.012 .854 ±.011 .852 ±.017 .514 ±.007 .859 ±.013 .831 ±.035 .855 ±.017 .856 ±.011 .846 ±.016 .850 ±.011 .825 ±.037 .823 ±.029 .858 ±.011 .840 ±.010 5 .862 ±.009 .863 ±.013 .861 ±.010 .860 ±.018 .768 ±.021 .861 ±.010 .859 ±.016 .860 ±.018 .861 ±.010 .860 ±.020 .861 ±.012 .856 ±.014 .854 ±.013 .865 ±.012 .856 ±.011 lymphography 1 .879 ±.050 .833 ±.110 .838 ±.061 .830 ±.034 .518 ±.032 .842 ±.117 .843 ±.127 .788 ±.018 .882 ±.058 .900 ±.044 .864 ±.096 .773 ±.169 .847 ±.093 .886 ±.054 .851 ±.087 2 .924 ±.014 .858 ±.112 .889 ±.059 .871 ±.021 .722 ±.167 .856 ±.110 .880 ±.084 .835 ±.015 .901 ±.043 .869 ±.063 .894 ±.064 .710 ±.235 .901 ±.033 .927 ±.024 .860 ±.076 3 .916 ±.024 .877 ±.085 .891 ±.063 .896 ±.036 .853 ±.111 .874 ±.083 .877 ±.114 .885 ±.005 .902 ±.033 .874 ±.055 .899 ±.070 .865 ±.041 .905 ±.049 .914 ±.033 .857 ±.098 4 .913 ±.031 .908 ±.034 .913 ±.025 .902 ±.048 .900 ±.070 .908 ±.033 .910 ±.057 .905 ±.029 .896 ±.046 .872 ±.065 .919 ±.039 .882 ±.066 .893 ±.069 .921 ±.037 .857 ±.099 5 .918 ±.040 .912 ±.034 .916 ±.047 .895 ±.054 .896 ±.086 .909 ±.031 .905 ±.069 .896 ±.038 .902 ±.047 .896 ±.077 .917 ±.037 .913 ±.076 .886 ±.069 .916 ±.052 .866 ±.098 nci_ovarian cancer 1 .987 ±.014 .985 ±.023 .978 ±.030 .980 ±.032 .558 ±.084 1.00 ±.00 .830 ±.067 .980 ±.035 .975 ±.028 .998 ±.001 .978 ±.030 .795 ±.180 .779 ±.127 .995 ±.009 .993 ±.002 2 .987 ±.013 1.00 ±.00 .999 ±.001 .999 ±.001 .755 ±.134 1.00 ±.00 .952 ±.023 .998 ±.003 .999 ±.001 .999 ±.001 .998 ±.002 .961 ±.032 .956 ±.059 .999 ±.001 .997 ±.004 3 .999 ±.001 1.00 ±.00 1.00 ±.00 1.00 ±.00 .805 ±.134 1.00 ±.00 .994 ±.003 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 .989 ±.011 .997 ±.001 1.00 ±.00 .998 ±.002 4 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 .943 ±.030 1.00 ±.00 .999 ±.001 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 .999 ±.001 .999 ±.001 1.00 ±.00 .999 ±.001 5 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 .970 ±.010 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 bad_customer detection 1 .578 ±.010 .609 ±.045 .629 ±.058 .712 ±.024 .664 ±.051 .609 ±.045 .651 ±.066 .606 ±.060 .681 ±.038 .682 ±.021 .613 ±.041 .615 ±.035 .689 ±.019 .578 ±.010 .706 ±.030 2 .690 ±.033 .578 ±.050 .643 ±.020 .709 ±.022 .658 ±.049 .578 ±.050 .633 ±.086 .668 ±.013 .715 ±.032 .700 ±.012 .628 ±.003 .567 ±.039 .706 ±.032 .690 ±.033 .715 ±.029 3 .696 ±.031 .644 ±.011 .638 ±.026 .700 ±.024 .653 ±.034 .644 ±.011 .698 ±.019 .698 ±.029 .716 ±.021 .699 ±.038 .616 ±.023 .685 ±.054 .686 ±.036 .689 ±.025 .701 ±.023 4 .707 ±.018 .653 ±.009 .654 ±.028 .700 ±.027 .676 ±.030 .653 ±.009 .700 ±.018 .699 ±.030 .722 ±.020 .713 ±.033 .653 ±.010 .680 ±.060 .701 ±.029 .701 ±.024 .698 ±.023 5 .709 ±.020 .710 ±.019 .710 ±.028 .731 ±.026 .685 ±.037 .710 ±.019 .706 ±.021 .709 ±.023 .718 ±.019 .717 ±.031 .670 ±.016 .718 ±.031 .701 ±.029 .730 ±.024 .706 ±.021 hazelnut_spread contaminant_detection_10GHz 1 .931 ±.017 .917 ±.011 .916 ±.010 .916 ±.012 .881 ±.010 .914 ±.014 .921 ±.019 .922 ±.013 .917 ±.011 .916 ±.015 .927 ±.007 .861 ±.008 .925 ±.007 .928 ±.017 .916 ±.013 2 .956 ±.017 .950 ±.010 .946 ±.012 .950 ±.012 .913 ±.016 .950 ±.010 .959 ±.014 .951 ±.015 .949 ±.013 .947 ±.009 .953 ±.007 .923 ±.020 .959 ±.009 .953 ±.011 .953 ±.016 3 .967 ±.008 .960 ±.007 .964 ±.010 .957 ±.010 .939 ±.018 .961 ±.007 .964 ±.010 .966 ±.010 .957 ±.011 .957 ±.006 .966 ±.010 .949 ±.022 .969 ±.010 .963 ±.009 .969 ±.012 4 .970 ±.007 .967 ±.009 .965 ±.010 .967 ±.010 .954 ±.012 .967 ±.009 .968 ±.010 .969 ±.010 .967 ±.013 .965 ±.007 .971 ±.010 .965 ±.017 .971 ±.011 .970 ±.009 .972 ±.012 5 .971 ±.009 .972 ±.009 .967 ±.010 .970 ±.011 .963 ±.011 .972 ±.009 .971 ±.009 .972 ±.008 .970 ±.012 .972 ±.009 .972 ±.009 .968 ±.012 .971 ±.010 .971 ±.009 .974 ±.010 drug_induced autoimmunity_prediction 1 .657 ±.042 .646 ±.049 .594 ±.076 .636 ±.031 .564 ±.057 .651 ±.080 .670 ±.025 .689 ±.028 .679 ±.044 .670 ±.039 .605 ±.078 .646 ±.066 .584 ±.030 .681 ±.019 .648 ±.034 2 .707 ±.041 .701 ±.041 .622 ±.008 .717 ±.010 .605 ±.032 .652 ±.015 .704 ±.028 .723 ±.027 .682 ±.040 .701 ±.024 .635 ±.026 .697 ±.035 .692 ±.026 .693 ±.040 .703 ±.047 3 .745 ±.032 .752 ±.009 .726 ±.010 .779 ±.026 .628 ±.021 .729 ±.070 .754 ±.014 .798 ±.040 .727 ±.034 .742 ±.047 .745 ±.025 .721 ±.041 .759 ±.028 .785 ±.037 .739 ±.008 4 .787 ±.014 .791 ±.013 .760 ±.031 .773 ±.024 .720 ±.027 .775 ±.035 .769 ±.015 .796 ±.032 .784 ±.054 .769 ±.012 .749 ±.021 .737 ±.019 .740 ±.009 .780 ±.031 .763 ±.042 5 .803 ±.005 .806 ±.031 .773 ±.022 .784 ±.025 .766 ±.025 .788 ±.016 .763 ±.012 .777 ±.035 .778 ±.027 .772 ±.020 .786 ±.028 .773 ±.010 .783 ±.030 .787 ±.020 .792 ±.039 heart_disease switzerland 1 .491 ±.219 .459 ±.172 .488 ±.221 .435 ±.118 .625 ±.126 .459 ±.172 .373 ±.031 .535 ±.267 .500 ±.000 .476 ±.135 .623 ±.058 .449 ±.277 .434 ±.183 .358 ±.074 .396 ±.120 2 .380 ±.177 .520 ±.333 .515 ±.259 .574 ±.230 .572 ±.049 .520 ±.333 .459 ±.169 .563 ±.252 .615 ±.055 .656 ±.261 .625 ±.165 .459 ±.191 .449 ±.145 .365 ±.261 .394 ±.140 3 .540 ±.228 .440 ±.311 .548 ±.267 .507 ±.243 .489 ±.171 .440 ±.311 .478 ±.214 .446 ±.270 .511 ±.267 .526 ±.122 .521 ±.285 .557 ±.215 .439 ±.130 .376 ±.180 .441 ±.217 4 .432 ±.281 .494 ±.287 .520 ±.277 .424 ±.191 .471 ±.258 .494 ±.287 .537 ±.232 .483 ±.237 .561 ±.193 .573 ±.233 .552 ±.346 .480 ±.163 .401 ±.143 .483 ±.158 .480 ±.198 5 .487 ±.195 .493 ±.301 .466 ±.182 .497 ±.219 .485 ±.217 .493 ±.301 .475 ±.247 .439 ±.184 .493 ±.241 .583 ±.287 .475 ±.192 .554 ±.235 .490 ±.267 .514 ±.197 .511 ±.242 hr analytics 1 .721 ±.001 .785 ±.006 .785 ±.006 .768 ±.025 .645 ±.006 .785 ±.006 .687 ±.002 .785 ±.006 .719 ±.005 .785 ±.006 .720 ±.002 .580 ±.046 .785 ±.006 .721 ±.001 .783 ±.004 2 .784 ±.006 .793 ±.007 .791 ±.007 .783 ±.009 .670 ±.004 .793 ±.007 .695 ±.004 .787 ±.007 .787 ±.008 .794 ±.006 .765 ±.042 .721 ±.023 .794 ±.008 .728 ±.005 .792 ±.004 3 .789 ±.006 .795 ±.006 .793 ±.010 .791 ±.007 .696 ±.019 .795 ±.006 .734 ±.049 .792 ±.004 .791 ±.007 .794 ±.007 .793 ±.013 .794 ±.006 .799 ±.009 .789 ±.006 .795 ±.008 4 .796 ±.005 .798 ±.009 .799 ±.007 .800 ±.007 .707 ±.014 .798 ±.009 .766 ±.043 .800 ±.007 .801 ±.007 .797 ±.006 .799 ±.010 .793 ±.010 .801 ±.009 .795 ±.003 .799 ±.008 5 .800 ±.006 .801 ±.005 .801 ±.010 .802 ±.007 .803 ±.007 .801 ±.005 .799 ±.011 .802 ±.007 .803 ±.007 .801 ±.006 .801 ±.010 .799 ±.008 .802 ±.007 .800 ±.006 .803 ±.006 porto seguro 1 .609 ±.004 .617 ±.003 .606 ±.004 .606 ±.002 .549 ±.003 .607 ±.013 .602 ±.010 .616 ±.003 .596 ±.002 .600 ±.006 .600 ±.001 .596 ±.007 .594 ±.009 .606 ±.001 .587 ±.003 2 .612 ±.001 .628 ±.006 .623 ±.005 .625 ±.001 .598 ±.002 .619 ±.013 .614 ±.001 .626 ±.006 .618 ±.002 .617 ±.004 .610 ±.001 .614 ±.005 .614 ±.003 .612 ±.001 .605 ±.008 3 .617 ±.001 .634 ±.005 .630 ±.002 .629 ±.007 .611 ±.003 .628 ±.007 .619 ±.003 .622 ±.001 .627 ±.001 .620 ±.003 .615 ±.002 .616 ±.004 .622 ±.009 .622 ±.001 .618 ±.007 4 .628 ±.001 .637 ±.002 .634 ±.001 .625 ±.002 .624 ±.004 .632 ±.005 .622 ±.007 .625 ±.002 .628 ±.000 .629 ±.002 .620 ±.000 .627 ±.007 .624 ±.009 .626 ±.001 .624 ±.001 5 .630 ±.000 .636 ±.005 .631 ±.000 .629 ±.001 .624 ±.002 .633 ±.004 .628 ±.001 .628 ±.002 .630 ±.001 .640 ±.001 .629 ±.000 .629 ±.000 .628 ±.003 .631 ±.000 .630 ±.008 japanese_credit screening 1 .814 ±.071 .751 ±.157 .816 ±.074 .804 ±.027 .431 ±.068 .814 ±.071 .683 ±.154 .829 ±.066 .804 ±.029 .796 ±.031 .610 ±.191 .577 ±.191 .675 ±.137 .844 ±.051 .698 ±.156 2 .822 ±.077 .741 ±.143 .827 ±.065 .798 ±.015 .479 ±.063 .808 ±.049 .679 ±.152 .801 ±.021 .790 ±.018 .778 ±.060 .839 ±.043 .707 ±.069 .667 ±.126 .819 ±.040 .693 ±.165 3 .828 ±.069 .794 ±.013 .816 ±.042 .799 ±.022 .528 ±.087 .820 ±.040 .689 ±.162 .794 ±.014 .789 ±.038 .793 ±.046 .795 ±.039 .685 ±.045 .677 ±.109 .800 ±.022 .693 ±.165 4 .813 ±.016 .798 ±.023 .828 ±.059 .790 ±.017 .713 ±.076 .786 ±.002 .686 ±.160 .796 ±.027 .795 ±.017 .806 ±.035 .790 ±.032 .794 ±.031 .688 ±.114 .794 ±.015 .685 ±.137 5 .796 ±.010 .784 ±.019 .805 ±.027 .792 ±.012 .837 ±.067 .790 ±.028 .702 ±.125 .792 ±.013 .799 ±.019 .787 ±.013 .791 ±.033 .781 ±.022 .702 ±.125 .800 ±.025 .702 ±.125 ionosphere 1 .955 ±.022 .929 ±.060 .951 ±.022 .963 ±.019 .913 ±.055 .921 ±.070 .937 ±.049 .946 ±.025 .945 ±.034 .955 ±.031 .952 ±.025 .923 ±.013 .945 ±.032 .962 ±.008 .962 ±.015 2 .956 ±.020 .947 ±.035 .968 ±.023 .960 ±.026 .927 ±.054 .946 ±.037 .943 ±.043 .959 ±.031 .964 ±.029 .966 ±.016 .960 ±.027 .954 ±.039 .952 ±.033 .962 ±.029 .959 ±.029 3 .962 ±.022 .960 ±.031 .959 ±.023 .968 ±.016 .939 ±.051 .961 ±.033 .947 ±.035 .962 ±.033 .967 ±.025 .966 ±.023 .964 ±.021 .959 ±.030 .951 ±.044 .963 ±.030 .959 ±.027 4 .965 ±.020 .968 ±.025 .957 ±.026 .970 ±.018 .955 ±.030 .965 ±.030 .955 ±.037 .967 ±.029 .967 ±.026 .965 ±.026 .966 ±.023 .960 ±.028 .951 ±.046 .964 ±.024 .958 ±.028 5 .964 ±.025 .968 ±.024 .964 ±.025 .966 ±.027 .963 ±.024 .966 ±.025 .965 ±.024 .970 ±.027 .965 ±.026 .969 ±.023 .966 ±.023 .968 ±.020 .967 ±.026 .965 ±.024 .963 ±.022 smoking lung_cancer 1 .664 ±.042 .684 ±.044 .688 ±.064 .663 ±.063 .623 ±.035 .688 ±.035 .656 ±.071 .660 ±.107 .694 ±.090 .672 ±.065 .688 ±.033 .559 ±.027 .507 ±.021 .716 ±.055 .559 ±.076 2 .737 ±.025 .752 ±.031 .694 ±.056 .731 ±.044 .708 ±.041 .728 ±.050 .670 ±.030 .685 ±.029 .733 ±.078 .744 ±.018 .735 ±.039 .661 ±.072 .654 ±.084 .720 ±.052 .673 ±.061 3 .737 ±.021 .764 ±.015 .728 ±.026 .719 ±.035 .731 ±.027 .733 ±.059 .710 ±.034 .728 ±.009 .715 ±.061 .728 ±.049 .757 ±.037 .695 ±.066 .707 ±.021 .747 ±.063 .711 ±.055 4 .729 ±.034 .764 ±.024 .744 ±.027 .744 ±.018 .722 ±.021 .751 ±.014 .724 ±.088 .742 ±.027 .716 ±.061 .743 ±.027 .752 ±.016 .748 ±.040 .741 ±.065 .755 ±.024 .742 ±.061 5 .730 ±.062 .741 ±.026 .735 ±.037 .745 ±.021 .725 ±.034 .740 ±.032 .717 ±.041 .745 ±.036 .735 ±.036 .740 ±.029 .742 ±.027 .739 ±.052 .715 ±.040 .752 ±.038 .715 ±.032 nci_pancreatic cancer 1 .588 ±.055 .674 ±.045 .593 ±.131 .572 ±.036 .501 ±.120 .582 ±.132 .512 ±.036 .541 ±.070 .512 ±.097 .531 ±.052 .579 ±.056 .509 ±.010 .494 ±.028 .579 ±.026 .490 ±.084 2 .591 ±.039 .731 ±.035 .638 ±.039 .580 ±.031 .512 ±.108 .690 ±.027 .529 ±.023 .597 ±.038 .565 ±.039 .663 ±.029 .626 ±.078 .559 ±.038 .577 ±.016 .630 ±.044 .513 ±.034 3 .610 ±.024 .719 ±.039 .650 ±.030 .558 ±.053 .542 ±.076 .707 ±.056 .574 ±.072 .586 ±.036 .561 ±.075 .655 ±.028 .638 ±.044 .587 ±.042 .653 ±.046 .637 ±.054 .657 ±.072 4 .631 ±.036 .705 ±.066 .683 ±.025 .614 ±.062 .540 ±.077 .701 ±.074 .615 ±.011 .602 ±.028 .595 ±.055 .677 ±.070 .613 ±.041 .597 ±.007 .597 ±.019 .660 ±.044 .589 ±.038 5 .651 ±.028 .695 ±.066 .655 ±.067 .597 ±.010 .517 ±.041 .659 ±.053 .628 ±.023 .578 ±.022 .596 ±.066 .651 ±.051 .620 ±.065 .616 ±.064 .609 ±.059 .661 ±.022 .612 ±.021 54 (continued from previous page) DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCO MarkovBlanket MImRMR(R)ReliefFRandomSBERFImportanceSFS homesite_quote conversion 1 .902 ±.001 .816 ±.002 .875 ±.001 .711 ±.091 .502 ±.000 .816 ±.002 .799 ±.045 .880 ±.019 .891 ±.001 .902 ±.001 .862 ±.037 .603 ±.038 .605 ±.022 .903 ±.001 .848 ±.015 2 .944 ±.005 .929 ±.001 .885 ±.001 .871 ±.030 .611 ±.091 .929 ±.001 .856 ±.048 .920 ±.001 .909 ±.001 .908 ±.001 .920 ±.001 .725 ±.062 .725 ±.052 .912 ±.001 .871 ±.008 3 .957 ±.002 .951 ±.001 .910 ±.001 .916 ±.000 .676 ±.001 .950 ±.000 .924 ±.036 .932 ±.000 .928 ±.002 .923 ±.001 .939 ±.001 .774 ±.056 .780 ±.054 .956 ±.001 .904 ±.003 4 .962 ±.001 .962 ±.000 .955 ±.000 .932 ±.000 .702 ±.001 .959 ±.003 .944 ±.019 .936 ±.002 .941 ±.000 .939 ±.005 .946 ±.001 .867 ±.025 .855 ±.009 .961 ±.001 .905 ±.008 5 .965 ±.001 .966 ±.000 .961 ±.000 .941 ±.000 .754 ±.006 .966 ±.000 .931 ±.014 .945 ±.002 .965 ±.000 .964 ±.000 .960 ±.001 .947 ±.010 .926 ±.026 .962 ±.001 .944 ±.006 heart_disease va_long_beach 1 .575 ±.067 .646 ±.128 .647 ±.141 .611 ±.128 .671 ±.115 .646 ±.128 .620 ±.075 .628 ±.112 .618 ±.118 .609 ±.111 .605 ±.115 .575 ±.055 .618 ±.115 .564 ±.061 .619 ±.075 2 .611 ±.110 .666 ±.130 .617 ±.065 .654 ±.119 .648 ±.104 .666 ±.130 .605 ±.067 .611 ±.075 .651 ±.109 .609 ±.111 .582 ±.114 .578 ±.070 .663 ±.098 .557 ±.031 .614 ±.068 3 .604 ±.071 .642 ±.130 .621 ±.068 .630 ±.101 .696 ±.142 .642 ±.130 .640 ±.099 .659 ±.103 .660 ±.113 .639 ±.171 .631 ±.111 .659 ±.057 .665 ±.099 .621 ±.065 .628 ±.083 4 .660 ±.093 .639 ±.134 .649 ±.082 .647 ±.089 .702 ±.138 .639 ±.134 .665 ±.097 .646 ±.101 .667 ±.089 .666 ±.176 .650 ±.111 .630 ±.105 .675 ±.099 .648 ±.089 .668 ±.094 5 .648 ±.087 .633 ±.076 .666 ±.098 .644 ±.092 .694 ±.099 .633 ±.076 .652 ±.081 .644 ±.092 .675 ±.083 .652 ±.090 .645 ±.092 .635 ±.097 .673 ±.101 .644 ±.092 .664 ±.097 qsar_biodeg 1 .899 ±.018 .885 ±.014 .856 ±.017 .888 ±.011 .660 ±.054 .873 ±.011 .880 ±.026 .910 ±.007 .889 ±.028 .908 ±.009 .902 ±.003 .874 ±.024 .899 ±.015 .873 ±.015 .911 ±.023 2 .921 ±.005 .920 ±.006 .910 ±.014 .909 ±.006 .884 ±.012 .906 ±.008 .912 ±.009 .918 ±.004 .913 ±.011 .910 ±.008 .921 ±.011 .896 ±.016 .917 ±.013 .910 ±.011 .916 ±.019 3 .920 ±.008 .922 ±.015 .921 ±.008 .923 ±.011 .910 ±.011 .917 ±.014 .918 ±.008 .921 ±.004 .922 ±.010 .917 ±.011 .919 ±.009 .919 ±.006 .924 ±.004 .922 ±.009 .923 ±.016 4 .921 ±.006 .925 ±.014 .925 ±.012 .925 ±.007 .915 ±.008 .924 ±.015 .920 ±.008 .924 ±.005 .925 ±.008 .929 ±.007 .926 ±.008 .926 ±.010 .924 ±.006 .923 ±.007 .925 ±.009 5 .924 ±.008 .925 ±.008 .926 ±.008 .925 ±.007 .922 ±.012 .925 ±.011 .925 ±.007 .926 ±.007 .925 ±.008 .930 ±.010 .926 ±.008 .924 ±.007 .925 ±.007 .926 ±.008 .928 ±.007 naticusdroid_android permissions_dataset 1 .979 ±.000 .977 ±.004 .978 ±.001 .849 ±.052 .581 ±.001 .969 ±.011 .973 ±.005 .782 ±.092 .974 ±.006 .849 ±.052 .972 ±.001 .885 ±.069 .849 ±.075 .979 ±.001 .973 ±.005 2 .982 ±.001 .982 ±.002 .981 ±.002 .936 ±.002 .633 ±.003 .980 ±.004 .978 ±.002 .877 ±.047 .981 ±.001 .936 ±.002 .979 ±.000 .886 ±.012 .932 ±.019 .983 ±.001 .975 ±.006 3 .983 ±.001 .984 ±.002 .983 ±.002 .956 ±.009 .724 ±.010 .983 ±.001 .980 ±.003 .954 ±.005 .983 ±.002 .956 ±.009 .982 ±.001 .950 ±.008 .949 ±.022 .984 ±.001 .980 ±.002 4 .984 ±.001 .985 ±.002 .984 ±.002 .978 ±.005 .822 ±.002 .984 ±.001 .981 ±.003 .967 ±.001 .984 ±.002 .978 ±.005 .984 ±.001 .957 ±.029 .960 ±.033 .985 ±.001 .982 ±.001 5 .985 ±.001 .985 ±.001 .984 ±.002 .983 ±.001 .954 ±.007 .985 ±.001 .982 ±.002 .981 ±.003 .984 ±.001 .983 ±.001 .984 ±.001 .977 ±.005 .979 ±.008 .985 ±.001 .984 ±.003 framingham heart_study 1 .600 ±.059 .676 ±.033 .658 ±.012 .666 ±.016 .558 ±.021 .674 ±.033 .662 ±.013 .648 ±.022 .661 ±.017 .671 ±.008 .659 ±.019 .574 ±.021 .665 ±.004 .625 ±.031 .673 ±.008 2 .656 ±.025 .686 ±.022 .663 ±.015 .664 ±.016 .589 ±.017 .686 ±.022 .683 ±.021 .654 ±.023 .660 ±.017 .688 ±.016 .674 ±.018 .630 ±.018 .690 ±.015 .673 ±.009 .677 ±.015 3 .688 ±.022 .691 ±.016 .689 ±.014 .683 ±.024 .642 ±.020 .696 ±.017 .696 ±.013 .682 ±.022 .670 ±.017 .695 ±.019 .681 ±.020 .657 ±.020 .696 ±.014 .688 ±.022 .685 ±.004 4 .697 ±.021 .694 ±.016 .696 ±.017 .689 ±.021 .689 ±.021 .694 ±.016 .695 ±.015 .689 ±.021 .681 ±.020 .693 ±.020 .689 ±.021 .690 ±.021 .695 ±.019 .699 ±.022 .693 ±.013 5 .703 ±.022 .698 ±.022 .701 ±.022 .703 ±.021 .695 ±.021 .696 ±.020 .700 ±.020 .695 ±.016 .696 ±.026 .696 ±.020 .698 ±.019 .682 ±.031 .697 ±.019 .703 ±.022 .697 ±.018 Table D.6: Per-cell results with downstream model Linear. Tasks are grouped by metric. Each cell is the mean over three seeds, with the sample standard deviation as a subscript. DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCO MarkovBlanket MImRMR(R)ReliefFRandomSBERFImportanceSFS log-loss (↓) horse_colic survival 1 .767 ±.068 .737 ±.075 .741 ±.061 .804 ±.050 .836 ±.053 .737 ±.075 .796 ±.070 .770 ±.030 .762 ±.088 .786 ±.061 .791 ±.050 .838 ±.068 .826 ±.119 .765 ±.067 .865 ±.040 2 .763 ±.070 .791 ±.093 .766 ±.049 .769 ±.044 .861 ±.106 .769 ±.066 .790 ±.068 .731 ±.054 .803 ±.100 .763 ±.040 .774 ±.047 .841 ±.112 .808 ±.105 .720 ±.050 .865 ±.085 3 .786 ±.059 .814 ±.085 .796 ±.016 .826 ±.048 .874 ±.080 .797 ±.085 .811 ±.121 .783 ±.045 .817 ±.108 .778 ±.041 .827 ±.030 .771 ±.047 .795 ±.023 .787 ±.018 .901 ±.085 4 .868 ±.080 .878 ±.078 .870 ±.032 .857 ±.053 .812 ±.053 .878 ±.065 .889 ±.184 .843 ±.066 .840 ±.018 .848 ±.024 .867 ±.013 .889 ±.037 .803 ±.017 .859 ±.014 .949 ±.109 5 .899 ±.033 .904 ±.048 .920 ±.034 .876 ±.018 .855 ±.042 .908 ±.069 .929 ±.142 .882 ±.017 .896 ±.047 .871 ±.041 .906 ±.037 .893 ±.107 .816 ±.015 .904 ±.019 .970 ±.100 srbct prediction 1 .659 ±.134 .273 ±.134 .496 ±.202 .561 ±.111 1.33 ±.13 .441 ±.189 1.28 ±.13 .730 ±.098 .538 ±.017 .465 ±.243 .362 ±.108 .958 ±.411 1.12 ±.36 .420 ±.149 1.15 ±.23 2 .618 ±.237 .119 ±.054 .267 ±.131 .233 ±.139 1.02 ±.28 .150 ±.040 .682 ±.272 .235 ±.112 .207 ±.104 .236 ±.088 .161 ±.020 1.53 ±.23 1.11 ±.25 .130 ±.012 1.09 ±.17 3 .396 ±.020 .0647 ±.0476 .0779 ±.0459 .0636 ±.0368 .713 ±.122 .0865 ±.0745 .423 ±.164 .0987 ±.0800 .0787 ±.0455 .0696 ±.0426 .0542 ±.0247 .374 ±.169 .398 ±.182 .0672 ±.0087 .391 ±.036 4 .160 ±.032 .0458 ±.0184 .0424 ±.0118 .0483 ±.0121 .428 ±.144 .0983 ±.0523 .193 ±.074 .0492 ±.0222 .0362 ±.0074 .0503 ±.0104 .0417 ±.0190 .250 ±.076 .189 ±.091 .0590 ±.0184 .280 ±.034 5 .133 ±.050 .0618 ±.0056 .0674 ±.0090 .0589 ±.0059 .186 ±.026 .0917 ±.0277 .132 ±.022 .0528 ±.0105 .0571 ±.0119 .0558 ±.0100 .0607 ±.0168 .120 ±.033 .112 ±.020 .0734 ±.0034 .120 ±.020 sdss_17 1 .170 ±.003 .187 ±.014 .223 ±.004 .228 ±.003 .732 ±.001 .352 ±.311 .172 ±.001 .172 ±.001 .207 ±.002 .232 ±.003 .221 ±.002 .606 ±.397 .170 ±.003 .228 ±.003 .207 ±.003 2 .158 ±.005 .161 ±.004 .171 ±.003 .159 ±.002 .624 ±.098 .159 ±.004 .159 ±.005 .162 ±.006 .147 ±.004 .219 ±.003 .171 ±.003 .181 ±.032 .157 ±.003 .160 ±.003 .158 ±.003 3 .142 ±.003 .156 ±.002 .163 ±.008 .142 ±.003 .581 ±.035 .156 ±.002 .155 ±.002 .142 ±.003 .142 ±.003 .170 ±.005 .157 ±.002 .641 ±.094 .155 ±.003 .147 ±.008 .145 ±.004 4 .142 ±.003 .154 ±.002 .160 ±.005 .141 ±.003 .279 ±.213 .154 ±.002 .150 ±.009 .141 ±.003 .142 ±.003 .152 ±.005 .143 ±.002 .415 ±.227 .154 ±.003 .142 ±.003 .142 ±.003 5 .142 ±.003 .155 ±.002 .151 ±.009 .142 ±.003 .143 ±.004 .155 ±.002 .141 ±.003 .142 ±.002 .142 ±.003 .142 ±.003 .142 ±.002 .275 ±.231 .141 ±.003 .142 ±.003 .141 ±.003 predict_students_dropout and_academic_success 1 .658 ±.031 .610 ±.017 .704 ±.015 .692 ±.010 .977 ±.022 .615 ±.018 .646 ±.037 .616 ±.003 .664 ±.019 .734 ±.019 .661 ±.020 .731 ±.045 .727 ±.036 .725 ±.030 .713 ±.020 2 .588 ±.006 .579 ±.020 .587 ±.014 .611 ±.015 .891 ±.022 .580 ±.021 .619 ±.025 .602 ±.010 .576 ±.024 .689 ±.012 .640 ±.006 .669 ±.034 .660 ±.077 .607 ±.013 .669 ±.020 3 .575 ±.010 .566 ±.013 .575 ±.023 .603 ±.009 .740 ±.019 .569 ±.009 .582 ±.025 .601 ±.004 .564 ±.018 .634 ±.015 .572 ±.017 .705 ±.015 .621 ±.058 .589 ±.015 .611 ±.029 4 .569 ±.023 .565 ±.013 .570 ±.014 .602 ±.009 .624 ±.018 .567 ±.014 .575 ±.029 .602 ±.009 .566 ±.020 .623 ±.011 .569 ±.017 .615 ±.017 .574 ±.021 .564 ±.021 .594 ±.052 5 .566 ±.019 .566 ±.018 .564 ±.016 .586 ±.024 .570 ±.016 .566 ±.020 .576 ±.031 .599 ±.013 .571 ±.018 .595 ±.017 .572 ±.015 .582 ±.036 .569 ±.026 .565 ±.022 .575 ±.012 ancestry study 1 1.61 ±.06 1.74 ±.10 1.71 ±.07 2.03 ±.15 2.12 ±.03 1.85 ±.23 1.78 ±.11 1.72 ±.05 1.69 ±.06 1.73 ±.04 1.71 ±.05 2.07 ±.14 2.11 ±.04 1.72 ±.05 1.84 ±.08 2 1.61 ±.10 1.66 ±.06 1.67 ±.09 1.83 ±.15 2.12 ±.03 1.80 ±.14 1.75 ±.10 1.73 ±.06 1.62 ±.09 1.75 ±.07 1.65 ±.07 2.13 ±.05 2.14 ±.06 1.74 ±.04 1.85 ±.08 3 1.76 ±.20 1.74 ±.07 1.63 ±.05 1.74 ±.06 2.04 ±.10 1.69 ±.10 1.80 ±.08 1.78 ±.04 1.67 ±.09 1.87 ±.06 1.68 ±.08 2.17 ±.10 2.06 ±.11 1.82 ±.09 1.82 ±.02 4 1.93 ±.10 1.86 ±.09 1.78 ±.07 1.91 ±.12 1.99 ±.03 1.75 ±.07 1.82 ±.07 2.08 ±.12 1.90 ±.12 2.11 ±.16 1.85 ±.20 2.05 ±.28 2.13 ±.05 1.91 ±.09 1.97 ±.04 5 2.03 ±.03 2.04 ±.08 1.89 ±.04 2.22 ±.15 1.95 ±.15 1.93 ±.03 2.00 ±.08 2.23 ±.17 1.98 ±.11 2.21 ±.14 2.03 ±.05 2.38 ±.13 2.12 ±.23 2.07 ±.08 2.19 ±.04 otto_group_product classification_challenge 1 1.07 ±.00 1.08 ±.01 1.16 ±.02 1.34 ±.01 1.25 ±.02 1.13 ±.01 1.25 ±.05 1.07 ±.00 1.19 ±.02 1.11 ±.01 1.26 ±.05 1.40 ±.06 1.38 ±.06 1.07 ±.00 1.36 ±.12 2 .899 ±.007 .909 ±.002 .964 ±.018 1.10 ±.01 1.01 ±.02 .924 ±.019 1.12 ±.10 .910 ±.003 .981 ±.003 .970 ±.001 1.02 ±.00 1.14 ±.02 1.11 ±.03 .911 ±.003 1.17 ±.06 3 .828 ±.007 .818 ±.001 .847 ±.003 .881 ±.003 .852 ±.005 .825 ±.002 1.01 ±.07 .830 ±.002 .884 ±.005 .873 ±.004 .905 ±.009 .976 ±.021 .971 ±.051 .828 ±.005 .987 ±.035 4 .790 ±.004 .780 ±.001 .796 ±.001 .817 ±.003 .804 ±.004 .787 ±.001 .878 ±.014 .791 ±.002 .798 ±.003 .798 ±.002 .828 ±.004 .889 ±.016 .890 ±.045 .783 ±.002 .895 ±.032 5 .766 ±.002 .764 ±.001 .769 ±.006 .769 ±.001 .768 ±.003 .764 ±.002 .795 ±.005 .765 ±.003 .766 ±.002 .770 ±.001 .786 ±.003 .813 ±.021 .835 ±.003 .765 ±.002 .826 ±.005 55 (continued from previous page) DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCO MarkovBlanket MImRMR(R)ReliefFRandomSBERFImportanceSFS lung_cancer 1 .294 ±.054 .402 ±.059 .535 ±.107 .352 ±.105 .816 ±.098 .440 ±.217 .618 ±.061 .348 ±.085 .411 ±.088 .316 ±.054 .378 ±.034 .761 ±.080 .737 ±.106 .340 ±.101 .615 ±.149 2 .263 ±.024 .216 ±.043 .380 ±.004 .221 ±.017 .824 ±.180 .285 ±.104 .354 ±.049 .227 ±.052 .221 ±.043 .213 ±.033 .209 ±.060 .507 ±.182 .556 ±.020 .220 ±.087 .455 ±.151 3 .185 ±.057 .153 ±.024 .181 ±.012 .155 ±.032 .663 ±.108 .203 ±.037 .245 ±.076 .188 ±.041 .166 ±.035 .160 ±.014 .186 ±.044 .159 ±.040 .227 ±.071 .161 ±.042 .262 ±.030 4 .176 ±.017 .140 ±.011 .155 ±.007 .150 ±.012 .482 ±.118 .157 ±.024 .162 ±.010 .155 ±.010 .146 ±.012 .150 ±.008 .171 ±.018 .173 ±.014 .159 ±.031 .148 ±.007 .160 ±.030 5 .154 ±.026 .155 ±.019 .158 ±.016 .142 ±.005 .253 ±.006 .155 ±.018 .155 ±.010 .145 ±.004 .144 ±.010 .162 ±.008 .155 ±.007 .165 ±.018 .163 ±.002 .160 ±.012 .163 ±.006 soybean large 1 .917 ±.048 1.52 ±.04 1.42 ±.19 1.45 ±.07 1.38 ±.17 1.39 ±.11 1.28 ±.17 .835 ±.052 .912 ±.067 .878 ±.044 1.49 ±.05 1.46 ±.10 1.17 ±.14 .816 ±.037 1.02 ±.15 2 .598 ±.034 1.13 ±.05 .957 ±.068 .856 ±.086 .833 ±.208 1.15 ±.06 .858 ±.084 .697 ±.092 .622 ±.041 .780 ±.061 1.08 ±.05 .947 ±.100 .805 ±.070 .572 ±.043 .703 ±.055 3 .595 ±.034 .804 ±.055 .803 ±.132 .622 ±.066 .661 ±.127 .930 ±.125 .734 ±.042 .633 ±.053 .462 ±.054 .675 ±.066 .725 ±.035 .609 ±.036 .608 ±.113 .568 ±.038 .585 ±.115 4 .535 ±.039 .618 ±.061 .648 ±.142 .514 ±.104 .543 ±.091 .725 ±.094 .612 ±.050 .533 ±.091 .452 ±.056 .593 ±.066 .575 ±.062 .585 ±.044 .554 ±.089 .506 ±.041 .528 ±.108 5 .496 ±.068 .496 ±.041 .516 ±.077 .476 ±.071 .479 ±.103 .632 ±.038 .533 ±.049 .520 ±.074 .463 ±.068 .509 ±.074 .474 ±.053 .533 ±.076 .490 ±.044 .479 ±.073 .511 ±.070 mic 1 .489 ±.007 .526 ±.015 .527 ±.027 .547 ±.014 .694 ±.017 .524 ±.026 .658 ±.041 .495 ±.001 .550 ±.012 .517 ±.025 .535 ±.019 .692 ±.008 .690 ±.014 .530 ±.032 .557 ±.035 2 .486 ±.019 .504 ±.021 .495 ±.006 .549 ±.009 .691 ±.017 .518 ±.033 .649 ±.038 .495 ±.003 .559 ±.016 .496 ±.033 .549 ±.020 .685 ±.016 .673 ±.044 .511 ±.019 .562 ±.039 3 .504 ±.022 .499 ±.005 .508 ±.012 .524 ±.020 .684 ±.022 .503 ±.006 .604 ±.034 .492 ±.016 .513 ±.028 .491 ±.022 .554 ±.024 .557 ±.021 .604 ±.080 .498 ±.024 .569 ±.032 4 .532 ±.023 .528 ±.030 .511 ±.007 .513 ±.015 .678 ±.004 .528 ±.025 .555 ±.033 .495 ±.014 .521 ±.039 .507 ±.038 .543 ±.007 .595 ±.021 .565 ±.038 .525 ±.038 .563 ±.050 5 .557 ±.055 .555 ±.043 .529 ±.014 .522 ±.035 .662 ±.028 .556 ±.042 .555 ±.055 .538 ±.024 .538 ±.046 .556 ±.028 .572 ±.034 .613 ±.037 .539 ±.038 .571 ±.063 .555 ±.084 ljubljana primary_tumor 1 1.74 ±.09 1.91 ±.14 1.87 ±.02 1.76 ±.09 2.04 ±.00 1.83 ±.08 1.85 ±.17 1.85 ±.11 1.77 ±.11 1.83 ±.11 1.92 ±.12 1.87 ±.14 1.80 ±.13 1.83 ±.11 1.82 ±.10 2 1.63 ±.14 1.74 ±.15 1.70 ±.09 1.63 ±.15 1.87 ±.10 1.76 ±.12 1.82 ±.19 1.66 ±.13 1.54 ±.15 1.63 ±.15 1.68 ±.11 1.81 ±.10 1.76 ±.21 1.67 ±.13 1.75 ±.17 3 1.46 ±.15 1.69 ±.18 1.56 ±.09 1.57 ±.17 1.83 ±.13 1.64 ±.14 1.54 ±.09 1.50 ±.18 1.45 ±.09 1.57 ±.17 1.61 ±.10 1.68 ±.22 1.60 ±.11 1.48 ±.22 1.62 ±.19 4 1.41 ±.19 1.62 ±.17 1.46 ±.12 1.48 ±.14 1.66 ±.16 1.56 ±.18 1.48 ±.17 1.50 ±.25 1.40 ±.16 1.48 ±.14 1.51 ±.10 1.52 ±.06 1.45 ±.17 1.37 ±.15 1.49 ±.17 5 1.41 ±.16 1.52 ±.17 1.49 ±.13 1.45 ±.15 1.55 ±.13 1.47 ±.18 1.45 ±.17 1.48 ±.20 1.41 ±.16 1.45 ±.15 1.51 ±.16 1.49 ±.24 1.46 ±.18 1.41 ±.16 1.44 ±.16 anneal 1 .144 ±.008 .217 ±.002 .268 ±.014 .156 ±.008 .525 ±.020 .209 ±.021 .195 ±.058 .158 ±.010 .187 ±.022 .184 ±.025 .191 ±.016 .623 ±.130 .169 ±.024 .152 ±.028 .158 ±.022 2 .110 ±.010 .119 ±.021 .170 ±.035 .136 ±.006 .433 ±.002 .115 ±.010 .129 ±.051 .134 ±.010 .139 ±.016 .131 ±.003 .130 ±.017 .340 ±.090 .124 ±.030 .122 ±.010 .121 ±.027 3 .106 ±.017 .101 ±.015 .111 ±.022 .111 ±.020 .372 ±.023 .100 ±.020 .113 ±.051 .117 ±.006 .116 ±.021 .111 ±.020 .112 ±.016 .233 ±.046 .0996 ±.0179 .103 ±.020 .122 ±.019 4 .102 ±.018 .100 ±.020 .104 ±.026 .104 ±.027 .251 ±.046 .0924 ±.0173 .103 ±.031 .0942 ±.0177 .107 ±.026 .104 ±.027 .0997 ±.0176 .212 ±.072 .0931 ±.0216 .0906 ±.0176 .111 ±.025 5 .104 ±.023 .0913 ±.0267 .0900 ±.0182 .0968 ±.0207 .0854 ±.0216 .0847 ±.0228 .0941 ±.0365 .0928 ±.0226 .0934 ±.0216 .0968 ±.0207 .0921 ±.0265 .0932 ±.0224 .0915 ±.0178 .0869 ±.0221 .101 ±.028 audiology diagnosis 1 .443 ±.157 .477 ±.130 .454 ±.122 .444 ±.147 .536 ±.077 .538 ±.073 .533 ±.049 .463 ±.117 .407 ±.130 .489 ±.107 .430 ±.120 .520 ±.061 .491 ±.137 .403 ±.132 .428 ±.092 2 .418 ±.189 .427 ±.139 .370 ±.159 .427 ±.184 .427 ±.130 .540 ±.028 .526 ±.093 .416 ±.137 .359 ±.123 .435 ±.133 .409 ±.124 .461 ±.215 .449 ±.098 .393 ±.213 .369 ±.128 3 .352 ±.246 .367 ±.135 .333 ±.138 .403 ±.165 .372 ±.093 .414 ±.103 .472 ±.117 .408 ±.195 .375 ±.176 .358 ±.098 .355 ±.154 .416 ±.125 .385 ±.048 .316 ±.180 .367 ±.143 4 .303 ±.187 .325 ±.149 .325 ±.141 .324 ±.138 .358 ±.126 .337 ±.137 .443 ±.128 .345 ±.103 .355 ±.190 .312 ±.105 .359 ±.184 .338 ±.109 .376 ±.054 .293 ±.185 .373 ±.163 5 .300 ±.212 .317 ±.161 .319 ±.157 .318 ±.196 .336 ±.162 .308 ±.158 .385 ±.171 .300 ±.138 .343 ±.229 .306 ±.187 .332 ±.194 .354 ±.160 .339 ±.129 .293 ±.187 .358 ±.169 RMSE (↓) qsar_tid_11 1 1.35 ±.02 1.44 ±.03 1.39 ±.03 1.53 ±.02 1.41 ±.03 1.45 ±.02 1.46 ±.05 1.53 ±.01 1.40 ±.04 1.53 ±.02 1.50 ±.01 1.53 ±.01 1.53 ±.01 1.35 ±.03 1.39 ±.01 2 1.29 ±.02 1.34 ±.02 1.37 ±.04 1.51 ±.01 1.38 ±.03 1.34 ±.02 1.41 ±.05 1.49 ±.05 1.35 ±.03 1.51 ±.01 1.43 ±.05 1.51 ±.02 1.52 ±.00 1.28 ±.01 1.37 ±.01 3 1.24 ±.03 1.23 ±.02 1.26 ±.02 1.45 ±.03 1.31 ±.04 1.21 ±.02 1.36 ±.02 1.42 ±.02 1.29 ±.03 1.45 ±.03 1.38 ±.04 1.42 ±.03 1.41 ±.05 1.21 ±.01 1.36 ±.02 4 1.14 ±.01 1.07 ±.02 1.14 ±.02 1.31 ±.02 1.20 ±.02 1.06 ±.02 1.27 ±.03 1.30 ±.01 1.18 ±.02 1.31 ±.02 1.21 ±.03 1.28 ±.06 1.30 ±.06 1.10 ±.02 1.24 ±.01 5 1.04 ±.00 .962 ±.022 1.02 ±.02 1.09 ±.03 1.04 ±.02 .963 ±.024 1.12 ±.01 1.13 ±.02 1.04 ±.04 1.09 ±.03 1.04 ±.00 1.10 ±.01 1.09 ±.02 1.00 ±.02 1.11 ±.01 forest fires 1 1.41 ±.02 1.41 ±.02 1.41 ±.02 1.40 ±.02 1.41 ±.05 1.41 ±.02 1.41 ±.03 1.41 ±.02 1.41 ±.05 1.40 ±.03 1.40 ±.02 1.40 ±.02 1.41 ±.02 1.41 ±.02 1.40 ±.03 2 1.40 ±.02 1.42 ±.02 1.43 ±.04 1.40 ±.02 1.41 ±.05 1.42 ±.02 1.41 ±.02 1.40 ±.02 1.41 ±.06 1.41 ±.05 1.41 ±.01 1.41 ±.02 1.41 ±.02 1.41 ±.02 1.42 ±.06 3 1.41 ±.02 1.41 ±.02 1.42 ±.04 1.42 ±.05 1.42 ±.05 1.41 ±.02 1.43 ±.04 1.41 ±.03 1.40 ±.05 1.42 ±.07 1.40 ±.04 1.42 ±.03 1.42 ±.02 1.40 ±.01 1.42 ±.08 4 1.41 ±.02 1.41 ±.03 1.43 ±.03 1.42 ±.04 1.42 ±.05 1.41 ±.03 1.41 ±.05 1.41 ±.03 1.40 ±.05 1.42 ±.05 1.41 ±.04 1.42 ±.04 1.44 ±.03 1.42 ±.02 1.42 ±.07 5 1.42 ±.06 1.42 ±.03 1.43 ±.04 1.42 ±.05 1.42 ±.05 1.42 ±.03 1.42 ±.05 1.41 ±.04 1.41 ±.04 1.42 ±.06 1.42 ±.04 1.43 ±.05 1.43 ±.05 1.43 ±.03 1.42 ±.06 allstate claims_severity 1 .679 ±.002 .681 ±.002 .677 ±.002 .712 ±.006 .811 ±.002 .681 ±.002 .672 ±.001 .702 ±.003 .672 ±.002 .722 ±.010 .695 ±.003 .784 ±.024 .787 ±.024 .679 ±.002 .668 ±.004 2 .655 ±.001 .655 ±.001 .662 ±.002 .662 ±.006 .808 ±.003 .655 ±.001 .648 ±.011 .654 ±.001 .654 ±.001 .685 ±.014 .656 ±.010 .789 ±.012 .797 ±.018 .658 ±.004 .656 ±.008 3 .608 ±.002 .619 ±.004 .649 ±.001 .619 ±.012 .807 ±.002 .619 ±.004 .625 ±.012 .643 ±.001 .630 ±.001 .644 ±.002 .643 ±.013 .740 ±.027 .746 ±.018 .609 ±.002 .639 ±.010 4 .585 ±.002 .592 ±.002 .628 ±.008 .598 ±.003 .778 ±.009 .592 ±.002 .590 ±.008 .634 ±.001 .596 ±.005 .633 ±.004 .606 ±.009 .688 ±.037 .686 ±.031 .585 ±.001 .635 ±.011 5 .572 ±.000 .571 ±.001 .583 ±.004 .584 ±.002 .699 ±.008 .571 ±.001 .571 ±.005 .579 ±.001 .571 ±.001 .616 ±.009 .588 ±.006 .623 ±.014 .634 ±.022 .572 ±.001 .602 ±.003 miami housing 1 .322 ±.020 .304 ±.003 .353 ±.004 .356 ±.004 .536 ±.007 .304 ±.003 .393 ±.014 .369 ±.004 .366 ±.027 .387 ±.006 .400 ±.002 .529 ±.055 .379 ±.002 .322 ±.021 .321 ±.004 2 .295 ±.003 .296 ±.004 .300 ±.003 .317 ±.004 .460 ±.002 .296 ±.004 .344 ±.025 .350 ±.014 .330 ±.003 .331 ±.027 .356 ±.039 .508 ±.018 .357 ±.027 .295 ±.003 .316 ±.006 3 .283 ±.005 .284 ±.005 .288 ±.003 .292 ±.003 .444 ±.001 .286 ±.000 .311 ±.007 .300 ±.013 .292 ±.002 .311 ±.006 .307 ±.004 .387 ±.091 .299 ±.011 .283 ±.005 .309 ±.006 4 .277 ±.002 .276 ±.002 .276 ±.004 .289 ±.003 .431 ±.003 .277 ±.001 .296 ±.021 .282 ±.007 .289 ±.003 .306 ±.004 .274 ±.005 .338 ±.060 .285 ±.015 .277 ±.004 .286 ±.014 5 .269 ±.005 .271 ±.005 .270 ±.005 .269 ±.005 .387 ±.004 .271 ±.005 .270 ±.006 .269 ±.005 .269 ±.005 .273 ±.005 .269 ±.005 .306 ±.055 .283 ±.017 .269 ±.005 .283 ±.017 wine quality 1 .750 ±.005 .790 ±.031 .783 ±.002 .783 ±.002 .832 ±.010 .793 ±.029 .843 ±.011 .750 ±.005 .783 ±.002 .777 ±.002 .771 ±.020 .795 ±.032 .777 ±.030 .750 ±.005 .777 ±.002 2 .744 ±.004 .742 ±.005 .746 ±.005 .765 ±.012 .771 ±.004 .742 ±.005 .840 ±.010 .747 ±.007 .772 ±.001 .764 ±.011 .758 ±.017 .822 ±.039 .759 ±.031 .742 ±.005 .746 ±.002 3 .735 ±.007 .736 ±.009 .744 ±.005 .743 ±.005 .738 ±.006 .736 ±.009 .781 ±.041 .742 ±.009 .749 ±.019 .757 ±.011 .744 ±.007 .774 ±.034 .750 ±.019 .732 ±.005 .742 ±.007 4 .734 ±.007 .734 ±.004 .742 ±.002 .736 ±.008 .738 ±.007 .734 ±.004 .754 ±.032 .738 ±.006 .736 ±.005 .748 ±.017 .740 ±.005 .762 ±.021 .745 ±.012 .731 ±.004 .737 ±.005 5 .730 ±.005 .730 ±.006 .734 ±.006 .732 ±.005 .735 ±.006 .730 ±.006 .735 ±.008 .735 ±.003 .733 ±.007 .737 ±.005 .731 ±.005 .746 ±.012 .735 ±.008 .731 ±.004 .735 ±.005 body_density prediction 1 .0114 ±.0014 .0108 ±.0015 .0112 ±.0011 .0110 ±.0013 .0122 ±.0032 .0108 ±.0015 .0121 ±.0025 .0109 ±.0013 .0110 ±.0013 .0112 ±.0015 .0112 ±.0017 .0143 ±.0041 .0112 ±.0014 .0113 ±.0015 .0109 ±.0013 2 .0112 ±.0018 .0113 ±.0011 .0107 ±.0011 .0107 ±.0011 .0109 ±.0009 .0113 ±.0011 .0123 ±.0026 .0107 ±.0011 .0108 ±.0011 .0111 ±.0013 .0109 ±.0011 .0127 ±.0017 .0111 ±.0015 .0108 ±.0015 .0113 ±.0011 3 .0109 ±.0012 .0112 ±.0010 .0108 ±.0010 .0107 ±.0012 .0110 ±.0008 .0112 ±.0010 .0111 ±.0010 .0108 ±.0013 .0108 ±.0010 .0112 ±.0014 .0110 ±.0010 .0118 ±.0023 .0110 ±.0014 .0107 ±.0014 .0110 ±.0008 4 .0110 ±.0015 .0113 ±.0011 .0110 ±.0011 .0108 ±.0009 .0110 ±.0007 .0113 ±.0011 .0110 ±.0011 .0110 ±.0011 .0109 ±.0014 .0111 ±.0013 .0109 ±.0009 .0123 ±.0007 .0106 ±.0010 .0110 ±.0016 .0107 ±.0008 5 .0112 ±.0012 .0111 ±.0012 .0111 ±.0010 .0111 ±.0010 .0109 ±.0007 .0111 ±.0012 .0110 ±.0010 .0112 ±.0010 .0112 ±.0010 .0111 ±.0013 .0110 ±.0009 .0125 ±.0005 .0108 ±.0008 .0109 ±.0017 .0108 ±.0009 brasilian houses ( × 10 3 ) 1 14.7 ±7.7 14.9 ±7.9 14.7 ±7.7 14.7 ±7.8 15.3 ±5.2 14.9 ±7.9 14.8 ±6.9 14.7 ±7.7 14.7 ±7.8 14.7 ±7.7 15.0 ±7.4 14.8 ±7.9 14.7 ±7.8 14.7 ±7.7 15.0 ±7.4 2 14.6 ±7.7 14.6 ±7.7 14.6 ±7.7 14.6 ±7.7 15.3 ±5.2 14.6 ±7.7 14.4 ±7.4 14.4 ±7.4 14.6 ±7.7 14.6 ±7.7 14.6 ±7.7 15.8 ±5.9 14.4 ±7.4 14.6 ±7.7 14.9 ±7.4 3 15.1 ±5.3 14.6 ±7.7 14.6 ±7.7 14.6 ±7.7 15.3 ±5.3 14.7 ±7.7 15.4 ±5.5 15.1 ±5.3 15.1 ±5.3 15.7 ±5.8 14.7 ±7.7 14.4 ±7.4 15.5 ±5.5 15.1 ±5.3 14.7 ±7.6 4 15.1 ±5.3 14.7 ±7.6 15.1 ±5.3 15.1 ±5.3 15.2 ±5.2 14.7 ±7.6 15.4 ±5.5 15.1 ±5.3 15.1 ±5.3 15.1 ±5.3 14.7 ±7.6 15.1 ±5.3 15.4 ±5.5 15.1 ±5.3 15.7 ±5.8 5 15.1 ±5.2 14.6 ±7.6 15.1 ±5.2 15.1 ±5.2 15.1 ±5.3 14.7 ±7.6 15.4 ±5.5 15.1 ±5.2 15.1 ±5.2 15.1 ±5.2 15.1 ±5.2 15.1 ±5.2 15.4 ±5.5 15.1 ±5.3 15.7 ±5.7 56 (continued from previous page) DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCO MarkovBlanket MImRMR(R)ReliefFRandomSBERFImportanceSFS automobile 1 .768 ±.122 .893 ±.061 .901 ±.035 .892 ±.073 1.10 ±.12 .917 ±.021 .878 ±.032 .860 ±.145 .831 ±.136 .870 ±.237 1.03 ±.07 .936 ±.094 .959 ±.079 .736 ±.086 1.02 ±.07 2 .735 ±.052 .883 ±.046 .898 ±.029 .815 ±.137 1.04 ±.10 .883 ±.046 .895 ±.029 .841 ±.090 .813 ±.189 .847 ±.148 1.01 ±.04 .918 ±.184 .900 ±.017 .731 ±.095 .911 ±.075 3 .708 ±.081 .906 ±.042 .901 ±.036 .839 ±.091 .900 ±.142 .906 ±.042 .848 ±.062 .749 ±.139 .732 ±.138 .802 ±.097 .788 ±.144 .787 ±.173 .797 ±.158 .730 ±.094 .770 ±.107 4 .719 ±.079 .918 ±.041 .801 ±.125 .789 ±.085 .868 ±.153 .918 ±.041 .749 ±.114 .749 ±.118 .734 ±.096 .764 ±.128 .827 ±.127 .789 ±.148 .792 ±.160 .731 ±.097 .782 ±.111 5 .736 ±.086 .896 ±.057 .845 ±.087 .801 ±.090 .852 ±.134 .896 ±.057 .745 ±.125 .764 ±.123 .749 ±.113 .752 ±.100 .784 ±.085 .769 ±.088 .751 ±.126 .753 ±.113 .774 ±.088 pva_revenue prediction_kddcup98 1 .599 ±.002 .597 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .597 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .612 ±.013 .598 ±.002 2 .612 ±.013 .597 ±.002 .597 ±.002 .598 ±.002 .599 ±.001 .597 ±.002 .598 ±.002 .598 ±.002 .599 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .618 ±.002 .598 ±.002 3 .619 ±.002 .597 ±.002 .597 ±.002 .598 ±.002 .599 ±.002 .597 ±.002 .598 ±.002 .598 ±.002 .600 ±.002 .598 ±.002 .598 ±.002 .604 ±.009 .604 ±.009 .618 ±.002 .598 ±.002 4 .620 ±.002 .597 ±.002 .597 ±.002 .599 ±.002 .599 ±.001 .597 ±.002 .599 ±.003 .606 ±.009 .609 ±.009 .597 ±.002 .597 ±.002 .598 ±.001 .598 ±.001 .619 ±.002 .598 ±.001 5 .623 ±.003 .597 ±.002 .597 ±.002 .602 ±.001 .600 ±.002 .597 ±.002 .606 ±.014 .614 ±.011 .609 ±.010 .597 ±.002 .598 ±.003 .612 ±.011 .613 ±.013 .623 ±.002 .612 ±.008 superconductivity 1 21.4 ±.3 20.5 ±.2 20.8 ±.2 20.7 ±.1 22.1 ±.2 21.0 ±.3 21.6 ±.1 20.9 ±1.0 20.8 ±.2 21.6 ±.2 21.5 ±.9 21.7 ±.4 21.9 ±.4 21.1 ±.5 21.5 ±.8 2 19.5 ±.1 18.7 ±.2 19.4 ±.1 19.6 ±.2 19.7 ±.1 19.1 ±.3 19.9 ±.6 19.6 ±.5 19.2 ±.2 20.1 ±.3 20.4 ±.2 20.3 ±.2 20.2 ±.5 18.8 ±.2 19.9 ±.1 3 18.3 ±.0 18.2 ±.2 18.7 ±.1 19.0 ±.1 19.0 ±.1 18.3 ±.1 19.0 ±.3 19.2 ±.6 18.9 ±.2 18.6 ±.3 19.0 ±.2 19.3 ±.6 19.1 ±.3 18.2 ±.1 18.8 ±.6 4 18.0 ±.1 17.9 ±.1 18.4 ±.1 18.2 ±.2 18.2 ±.1 17.8 ±.2 18.3 ±.1 18.5 ±.2 18.6 ±.1 18.0 ±.1 18.3 ±.3 18.3 ±.4 18.4 ±.2 17.8 ±.2 18.4 ±.2 5 17.8 ±.1 17.7 ±.1 17.9 ±.1 17.8 ±.1 17.8 ±.1 17.6 ±.2 17.8 ±.1 18.0 ±.2 17.8 ±.1 17.7 ±.1 17.6 ±.1 17.6 ±.1 17.7 ±.1 17.7 ±.1 17.7 ±.2 AUC (↑) in_vehicle_coupon recommendation 1 .668 ±.024 .696 ±.008 .692 ±.007 .695 ±.015 .575 ±.018 .698 ±.010 .699 ±.011 .695 ±.013 .699 ±.023 .693 ±.016 .661 ±.015 .581 ±.026 .684 ±.015 .675 ±.014 .706 ±.011 2 .693 ±.014 .705 ±.009 .702 ±.009 .700 ±.013 .594 ±.011 .705 ±.009 .718 ±.008 .699 ±.012 .720 ±.018 .710 ±.011 .680 ±.015 .623 ±.007 .717 ±.013 .696 ±.014 .718 ±.004 3 .700 ±.012 .719 ±.016 .709 ±.008 .704 ±.012 .643 ±.003 .721 ±.016 .722 ±.009 .702 ±.013 .723 ±.020 .721 ±.011 .699 ±.007 .671 ±.034 .728 ±.014 .698 ±.015 .726 ±.005 4 .722 ±.011 .723 ±.015 .724 ±.018 .710 ±.011 .671 ±.004 .724 ±.015 .725 ±.010 .710 ±.010 .725 ±.020 .729 ±.008 .719 ±.013 .720 ±.018 .730 ±.013 .730 ±.011 .732 ±.005 5 .733 ±.009 .736 ±.008 .731 ±.009 .727 ±.011 .734 ±.008 .736 ±.008 .731 ±.012 .722 ±.018 .728 ±.022 .734 ±.008 .732 ±.010 .730 ±.006 .732 ±.010 .734 ±.010 .733 ±.006 marketing campaign 1 .813 ±.040 .796 ±.043 .770 ±.020 .763 ±.032 .713 ±.025 .798 ±.044 .768 ±.019 .768 ±.023 .731 ±.029 .808 ±.042 .749 ±.005 .735 ±.009 .818 ±.043 .782 ±.026 .805 ±.054 2 .830 ±.044 .873 ±.031 .866 ±.021 .796 ±.028 .832 ±.009 .876 ±.031 .847 ±.003 .809 ±.002 .821 ±.006 .851 ±.034 .796 ±.046 .771 ±.041 .860 ±.024 .812 ±.031 .852 ±.014 3 .859 ±.030 .887 ±.028 .870 ±.026 .853 ±.018 .856 ±.009 .889 ±.026 .886 ±.019 .847 ±.015 .859 ±.017 .875 ±.019 .852 ±.014 .824 ±.025 .884 ±.013 .848 ±.026 .892 ±.024 4 .887 ±.021 .893 ±.022 .883 ±.022 .859 ±.012 .861 ±.013 .894 ±.024 .893 ±.018 .857 ±.010 .891 ±.009 .891 ±.013 .873 ±.024 .866 ±.042 .898 ±.012 .884 ±.013 .904 ±.016 5 .900 ±.014 .900 ±.017 .904 ±.010 .878 ±.013 .883 ±.024 .900 ±.017 .902 ±.017 .893 ±.005 .904 ±.012 .900 ±.008 .889 ±.015 .890 ±.032 .904 ±.013 .901 ±.013 .906 ±.013 polish_companies bankruptcy 1 .828 ±.032 .819 ±.006 .820 ±.012 .813 ±.014 .806 ±.008 .822 ±.005 .830 ±.010 .831 ±.009 .822 ±.018 .816 ±.017 .812 ±.008 .808 ±.023 .810 ±.017 .834 ±.015 .823 ±.027 2 .850 ±.020 .829 ±.013 .843 ±.005 .835 ±.010 .833 ±.008 .835 ±.015 .841 ±.027 .843 ±.016 .838 ±.019 .843 ±.007 .841 ±.015 .830 ±.021 .848 ±.016 .850 ±.007 .849 ±.008 3 .859 ±.015 .845 ±.008 .850 ±.008 .854 ±.006 .850 ±.006 .846 ±.005 .855 ±.015 .853 ±.011 .852 ±.013 .864 ±.008 .862 ±.013 .846 ±.016 .853 ±.007 .850 ±.007 .858 ±.011 4 .865 ±.002 .849 ±.013 .854 ±.012 .856 ±.007 .852 ±.011 .849 ±.014 .861 ±.020 .859 ±.013 .856 ±.007 .867 ±.012 .861 ±.016 .859 ±.019 .864 ±.011 .856 ±.010 .868 ±.005 5 .867 ±.010 .859 ±.015 .860 ±.012 .861 ±.010 .856 ±.014 .861 ±.014 .863 ±.020 .865 ±.011 .860 ±.012 .870 ±.013 .864 ±.017 .867 ±.014 .864 ±.010 .867 ±.010 .864 ±.014 colon_tumor 1 .748 ±.047 .862 ±.053 .876 ±.100 .840 ±.145 .675 ±.226 .824 ±.106 .769 ±.076 .850 ±.139 .854 ±.104 .814 ±.067 .888 ±.083 .633 ±.236 .702 ±.285 .820 ±.180 .723 ±.093 2 .835 ±.079 .882 ±.098 .891 ±.082 .884 ±.077 .896 ±.113 .885 ±.089 .898 ±.044 .843 ±.164 .874 ±.094 .898 ±.071 .895 ±.055 .889 ±.051 .890 ±.067 .891 ±.091 .823 ±.088 3 .861 ±.064 .886 ±.076 .878 ±.080 .878 ±.080 .906 ±.110 .893 ±.077 .864 ±.096 .888 ±.073 .879 ±.080 .878 ±.076 .891 ±.065 .815 ±.080 .829 ±.131 .872 ±.059 .881 ±.014 4 .872 ±.070 .893 ±.071 .895 ±.072 .886 ±.065 .912 ±.101 .895 ±.065 .898 ±.071 .896 ±.091 .889 ±.059 .879 ±.060 .878 ±.065 .897 ±.080 .891 ±.082 .894 ±.068 .894 ±.024 5 .905 ±.039 .889 ±.058 .895 ±.069 .909 ±.073 .911 ±.074 .909 ±.034 .896 ±.060 .907 ±.081 .902 ±.067 .899 ±.066 .895 ±.069 .875 ±.092 .915 ±.050 .892 ±.054 .905 ±.085 mechanisms of_action 1 .939 ±.009 .915 ±.037 .939 ±.010 .937 ±.006 .922 ±.010 .939 ±.010 .904 ±.046 .938 ±.009 .936 ±.006 .943 ±.014 .882 ±.078 .879 ±.025 .921 ±.023 .937 ±.002 .931 ±.007 2 .939 ±.010 .947 ±.012 .937 ±.005 .935 ±.002 .928 ±.013 .945 ±.010 .932 ±.005 .936 ±.007 .935 ±.002 .941 ±.011 .927 ±.024 .927 ±.014 .929 ±.013 .941 ±.010 .939 ±.007 3 .943 ±.011 .945 ±.013 .938 ±.003 .936 ±.006 .936 ±.018 .944 ±.013 .943 ±.006 .943 ±.008 .938 ±.006 .944 ±.009 .940 ±.013 .938 ±.009 .942 ±.010 .941 ±.009 .941 ±.009 4 .946 ±.009 .952 ±.011 .943 ±.007 .945 ±.005 .940 ±.012 .950 ±.005 .945 ±.007 .950 ±.007 .947 ±.010 .952 ±.009 .947 ±.012 .945 ±.006 .940 ±.007 .950 ±.013 .949 ±.004 5 .954 ±.009 .955 ±.002 .954 ±.006 .956 ±.005 .953 ±.006 .955 ±.006 .949 ±.010 .954 ±.005 .955 ±.005 .958 ±.004 .960 ±.003 .951 ±.008 .951 ±.008 .961 ±.006 .954 ±.008 santander_customer transaction_prediction 1 .613 ±.008 .614 ±.006 .615 ±.006 .615 ±.006 .501 ±.001 .614 ±.006 .578 ±.017 .615 ±.006 .502 ±.002 .615 ±.006 .505 ±.008 .515 ±.005 .532 ±.004 .615 ±.006 .571 ±.011 2 .671 ±.004 .675 ±.004 .677 ±.005 .675 ±.006 .501 ±.001 .675 ±.004 .610 ±.010 .674 ±.006 .509 ±.003 .675 ±.006 .509 ±.003 .568 ±.020 .572 ±.023 .672 ±.002 .584 ±.014 3 .747 ±.005 .752 ±.005 .753 ±.005 .748 ±.005 .516 ±.002 .752 ±.005 .645 ±.017 .750 ±.004 .523 ±.004 .752 ±.004 .514 ±.002 .592 ±.028 .590 ±.017 .751 ±.004 .596 ±.012 4 .821 ±.006 .830 ±.004 .829 ±.005 .825 ±.006 .530 ±.003 .830 ±.004 .657 ±.014 .821 ±.006 .550 ±.004 .822 ±.006 .531 ±.006 .631 ±.010 .619 ±.014 .827 ±.005 .658 ±.003 5 .860 ±.003 .860 ±.003 .860 ±.003 .860 ±.003 .551 ±.004 .860 ±.003 .736 ±.009 .852 ±.004 .658 ±.008 .846 ±.003 .571 ±.018 .737 ±.022 .730 ±.007 .859 ±.003 .745 ±.011 nci_prostate cancer 1 .945 ±.031 .936 ±.022 .937 ±.015 .935 ±.014 .564 ±.061 .892 ±.054 .692 ±.158 .792 ±.022 .934 ±.015 .936 ±.035 .774 ±.011 .661 ±.050 .699 ±.048 .924 ±.015 .724 ±.088 2 .949 ±.013 .956 ±.022 .949 ±.024 .948 ±.033 .653 ±.053 .977 ±.012 .828 ±.038 .908 ±.011 .955 ±.009 .946 ±.020 .938 ±.048 .769 ±.041 .834 ±.036 .958 ±.035 .830 ±.022 3 .961 ±.010 .973 ±.007 .962 ±.003 .971 ±.017 .689 ±.071 .987 ±.007 .932 ±.027 .954 ±.008 .978 ±.011 .960 ±.014 .972 ±.010 .902 ±.018 .908 ±.003 .983 ±.002 .949 ±.014 4 .975 ±.016 .989 ±.004 .971 ±.017 .975 ±.015 .839 ±.046 .991 ±.006 .967 ±.015 .982 ±.007 .986 ±.006 .978 ±.005 .978 ±.014 .965 ±.013 .963 ±.008 .988 ±.002 .963 ±.003 5 .983 ±.011 .987 ±.010 .979 ±.025 .988 ±.008 .936 ±.015 .988 ±.009 .979 ±.013 .986 ±.007 .990 ±.007 .988 ±.008 .990 ±.006 .977 ±.013 .978 ±.014 .986 ±.008 .979 ±.013 bank_customer churn 1 .732 ±.010 .744 ±.014 .744 ±.014 .731 ±.008 .593 ±.017 .744 ±.014 .731 ±.008 .731 ±.008 .731 ±.008 .731 ±.008 .628 ±.091 .549 ±.058 .731 ±.008 .732 ±.009 .731 ±.008 2 .739 ±.002 .762 ±.007 .762 ±.007 .747 ±.007 .644 ±.014 .762 ±.007 .748 ±.006 .747 ±.007 .756 ±.014 .755 ±.014 .731 ±.008 .675 ±.063 .745 ±.009 .739 ±.002 .759 ±.010 3 .738 ±.002 .770 ±.009 .767 ±.013 .747 ±.007 .678 ±.011 .770 ±.009 .757 ±.014 .747 ±.007 .764 ±.008 .755 ±.013 .738 ±.003 .697 ±.084 .748 ±.007 .738 ±.002 .769 ±.010 4 .754 ±.008 .772 ±.008 .772 ±.008 .747 ±.007 .678 ±.013 .772 ±.008 .769 ±.006 .747 ±.007 .772 ±.008 .770 ±.010 .745 ±.003 .724 ±.070 .773 ±.009 .749 ±.007 .772 ±.008 5 .773 ±.009 .772 ±.008 .773 ±.008 .758 ±.012 .680 ±.013 .772 ±.008 .772 ±.009 .762 ±.006 .773 ±.008 .771 ±.010 .755 ±.007 .732 ±.056 .772 ±.009 .770 ±.013 .773 ±.009 hepatitis_survival prediction 1 .787 ±.070 .771 ±.092 .768 ±.031 .812 ±.041 .816 ±.041 .804 ±.066 .847 ±.035 .852 ±.037 .788 ±.036 .838 ±.051 .824 ±.063 .839 ±.063 .785 ±.067 .752 ±.004 .828 ±.028 2 .791 ±.017 .821 ±.055 .820 ±.009 .818 ±.007 .820 ±.028 .821 ±.033 .838 ±.027 .834 ±.022 .833 ±.066 .850 ±.023 .827 ±.018 .804 ±.027 .816 ±.063 .782 ±.025 .843 ±.030 3 .832 ±.033 .841 ±.049 .822 ±.018 .832 ±.019 .842 ±.023 .852 ±.032 .858 ±.022 .828 ±.031 .841 ±.029 .838 ±.042 .845 ±.014 .845 ±.057 .837 ±.060 .831 ±.031 .820 ±.036 4 .838 ±.052 .849 ±.045 .846 ±.047 .844 ±.032 .851 ±.048 .846 ±.038 .863 ±.017 .839 ±.035 .856 ±.037 .842 ±.046 .842 ±.036 .865 ±.035 .855 ±.058 .837 ±.036 .838 ±.028 5 .857 ±.034 .849 ±.032 .845 ±.032 .846 ±.041 .854 ±.030 .843 ±.040 .869 ±.036 .847 ±.041 .840 ±.046 .843 ±.039 .841 ±.043 .867 ±.061 .851 ±.013 .834 ±.042 .859 ±.053 customer_satisfaction in_airline 1 .933 ±.000 .911 ±.000 .904 ±.001 .906 ±.000 .908 ±.002 .911 ±.000 .909 ±.026 .931 ±.004 .928 ±.001 .905 ±.001 .925 ±.001 .827 ±.044 .922 ±.005 .928 ±.001 .926 ±.006 2 .937 ±.000 .947 ±.003 .922 ±.004 .945 ±.000 .927 ±.001 .947 ±.003 .940 ±.002 .941 ±.000 .942 ±.000 .943 ±.000 .938 ±.000 .812 ±.020 .916 ±.055 .938 ±.000 .946 ±.002 3 .954 ±.003 .958 ±.001 .945 ±.013 .950 ±.000 .940 ±.001 .958 ±.001 .952 ±.004 .948 ±.000 .954 ±.000 .949 ±.001 .951 ±.001 .930 ±.021 .959 ±.001 .951 ±.003 .958 ±.001 4 .961 ±.002 .960 ±.001 .956 ±.001 .951 ±.000 .959 ±.000 .960 ±.001 .961 ±.000 .951 ±.000 .956 ±.000 .958 ±.001 .960 ±.001 .951 ±.009 .963 ±.001 .958 ±.003 .962 ±.001 5 .963 ±.000 .962 ±.001 .961 ±.001 .952 ±.000 .962 ±.001 .962 ±.001 .964 ±.000 .957 ±.000 .959 ±.002 .960 ±.000 .962 ±.002 .959 ±.005 .964 ±.000 .964 ±.000 .964 ±.000 57 (continued from previous page) DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCO MarkovBlanket MImRMR(R)ReliefFRandomSBERFImportanceSFS heloc 1 .741 ±.015 .744 ±.002 .740 ±.012 .734 ±.009 .663 ±.004 .747 ±.005 .710 ±.051 .722 ±.028 .739 ±.010 .718 ±.009 .698 ±.009 .659 ±.085 .712 ±.021 .733 ±.009 .745 ±.007 2 .751 ±.005 .772 ±.007 .754 ±.003 .758 ±.007 .758 ±.011 .776 ±.002 .758 ±.015 .763 ±.020 .760 ±.002 .756 ±.012 .750 ±.004 .730 ±.009 .754 ±.011 .752 ±.008 .761 ±.010 3 .764 ±.011 .780 ±.007 .760 ±.005 .776 ±.006 .760 ±.010 .779 ±.007 .771 ±.022 .777 ±.005 .772 ±.008 .778 ±.010 .767 ±.003 .749 ±.008 .772 ±.008 .760 ±.008 .774 ±.006 4 .775 ±.012 .785 ±.010 .781 ±.006 .780 ±.007 .774 ±.006 .784 ±.011 .782 ±.011 .782 ±.006 .779 ±.008 .783 ±.008 .774 ±.007 .778 ±.007 .777 ±.006 .775 ±.008 .778 ±.006 5 .783 ±.010 .784 ±.009 .784 ±.008 .784 ±.006 .782 ±.006 .784 ±.009 .785 ±.009 .784 ±.006 .783 ±.008 .783 ±.010 .780 ±.009 .783 ±.011 .781 ±.007 .783 ±.010 .784 ±.010 hiva agnostic 1 .607 ±.037 .665 ±.044 .548 ±.040 .533 ±.057 .539 ±.010 .589 ±.045 .518 ±.098 .515 ±.038 .583 ±.049 .533 ±.057 .625 ±.041 .579 ±.035 .576 ±.036 .595 ±.062 .681 ±.062 2 .676 ±.078 .713 ±.046 .607 ±.102 .621 ±.031 .573 ±.020 .748 ±.014 .630 ±.045 .606 ±.069 .612 ±.088 .621 ±.031 .651 ±.060 .622 ±.009 .608 ±.010 .693 ±.032 .697 ±.016 3 .697 ±.066 .732 ±.055 .644 ±.102 .680 ±.022 .608 ±.057 .759 ±.009 .696 ±.058 .682 ±.058 .683 ±.082 .680 ±.022 .727 ±.035 .712 ±.076 .672 ±.048 .745 ±.015 .717 ±.053 4 .700 ±.047 .732 ±.056 .771 ±.012 .660 ±.024 .670 ±.063 .698 ±.002 .705 ±.092 .721 ±.080 .694 ±.042 .660 ±.024 .766 ±.003 .731 ±.039 .719 ±.061 .764 ±.034 .705 ±.016 5 .699 ±.046 .729 ±.051 .751 ±.037 .701 ±.016 .706 ±.027 .711 ±.014 .704 ±.050 .758 ±.025 .730 ±.034 .701 ±.016 .726 ±.037 .722 ±.045 .732 ±.056 .750 ±.008 .730 ±.041 bank marketing 1 .637 ±.005 .690 ±.011 .689 ±.011 .661 ±.008 .626 ±.007 .689 ±.011 .687 ±.029 .657 ±.014 .661 ±.014 .616 ±.019 .675 ±.009 .673 ±.023 .707 ±.007 .634 ±.010 .714 ±.011 2 .689 ±.017 .714 ±.007 .719 ±.009 .687 ±.006 .652 ±.007 .714 ±.007 .719 ±.030 .690 ±.011 .719 ±.018 .669 ±.010 .684 ±.012 .683 ±.033 .723 ±.005 .682 ±.016 .729 ±.011 3 .703 ±.016 .730 ±.011 .732 ±.010 .707 ±.011 .730 ±.012 .730 ±.011 .732 ±.025 .708 ±.012 .727 ±.011 .714 ±.007 .704 ±.011 .710 ±.017 .739 ±.015 .703 ±.017 .733 ±.008 4 .729 ±.020 .739 ±.012 .742 ±.013 .721 ±.011 .736 ±.013 .736 ±.016 .745 ±.017 .721 ±.011 .743 ±.013 .741 ±.013 .736 ±.013 .728 ±.012 .745 ±.014 .713 ±.016 .744 ±.014 5 .747 ±.013 .744 ±.016 .742 ±.014 .728 ±.009 .742 ±.014 .746 ±.014 .747 ±.015 .744 ±.017 .746 ±.014 .747 ±.013 .742 ±.014 .734 ±.023 .745 ±.014 .747 ±.013 .747 ±.015 leukemia allaml 1 .890 ±.049 .975 ±.022 .987 ±.023 .985 ±.021 .692 ±.061 .969 ±.021 .739 ±.204 .983 ±.030 .990 ±.011 .985 ±.015 .995 ±.009 .730 ±.185 .664 ±.193 .995 ±.009 .616 ±.085 2 .934 ±.041 .962 ±.033 .972 ±.030 .982 ±.025 .829 ±.109 .985 ±.015 .781 ±.162 .879 ±.095 .995 ±.009 .974 ±.028 .979 ±.030 .700 ±.107 .836 ±.106 .988 ±.015 .821 ±.097 3 .954 ±.036 .982 ±.019 .975 ±.043 .995 ±.004 .935 ±.061 .978 ±.038 .942 ±.040 .950 ±.073 .988 ±.015 .995 ±.009 .987 ±.012 .939 ±.033 .970 ±.012 .995 ±.009 .934 ±.066 4 .980 ±.028 .983 ±.030 .978 ±.038 .985 ±.026 .962 ±.029 .978 ±.038 .983 ±.030 .983 ±.024 .985 ±.026 .993 ±.013 .985 ±.026 .968 ±.056 .980 ±.028 .998 ±.004 .975 ±.043 5 .980 ±.034 .980 ±.034 .980 ±.034 .980 ±.034 .980 ±.034 .975 ±.043 .983 ±.030 .983 ±.030 .980 ±.034 .980 ±.034 .980 ±.034 .973 ±.047 .988 ±.021 .980 ±.034 .980 ±.028 heart_disease cleveland 1 .851 ±.031 .848 ±.028 .809 ±.020 .851 ±.074 .769 ±.030 .857 ±.056 .861 ±.095 .823 ±.041 .857 ±.036 .850 ±.027 .864 ±.044 .836 ±.027 .848 ±.051 .839 ±.047 .838 ±.038 2 .870 ±.042 .875 ±.018 .878 ±.014 .900 ±.041 .811 ±.018 .870 ±.049 .871 ±.057 .850 ±.050 .888 ±.039 .868 ±.020 .885 ±.028 .860 ±.021 .849 ±.061 .892 ±.047 .865 ±.034 3 .872 ±.033 .897 ±.024 .885 ±.017 .894 ±.043 .860 ±.007 .888 ±.029 .876 ±.055 .881 ±.028 .899 ±.046 .878 ±.021 .897 ±.028 .825 ±.050 .870 ±.072 .894 ±.048 .896 ±.044 4 .884 ±.032 .900 ±.028 .904 ±.021 .901 ±.040 .878 ±.039 .906 ±.018 .879 ±.056 .901 ±.040 .903 ±.045 .903 ±.035 .898 ±.026 .910 ±.035 .869 ±.068 .891 ±.039 .905 ±.047 5 .899 ±.035 .903 ±.026 .906 ±.032 .896 ±.042 .898 ±.037 .906 ±.040 .898 ±.054 .903 ±.048 .904 ±.049 .902 ±.046 .907 ±.023 .893 ±.047 .899 ±.056 .906 ±.042 .899 ±.051 clock_protein period 1 .570 ±.070 .582 ±.110 .605 ±.089 .543 ±.160 .670 ±.046 .582 ±.110 .584 ±.151 .593 ±.106 .476 ±.121 .562 ±.095 .555 ±.125 .672 ±.123 .698 ±.078 .575 ±.104 .654 ±.106 2 .593 ±.077 .605 ±.115 .649 ±.115 .601 ±.153 .637 ±.109 .605 ±.115 .667 ±.050 .587 ±.054 .557 ±.072 .584 ±.135 .580 ±.083 .552 ±.169 .552 ±.146 .619 ±.132 .638 ±.105 3 .563 ±.150 .596 ±.137 .623 ±.169 .582 ±.225 .614 ±.151 .596 ±.137 .600 ±.123 .596 ±.185 .594 ±.111 .554 ±.135 .568 ±.096 .665 ±.101 .589 ±.143 .570 ±.162 .654 ±.135 4 .566 ±.071 .578 ±.090 .593 ±.176 .534 ±.199 .605 ±.073 .578 ±.090 .596 ±.116 .587 ±.112 .598 ±.102 .540 ±.191 .497 ±.181 .571 ±.166 .603 ±.155 .577 ±.134 .513 ±.128 5 .571 ±.106 .571 ±.104 .640 ±.176 .575 ±.169 .642 ±.101 .571 ±.104 .571 ±.142 .549 ±.136 .561 ±.102 .513 ±.178 .527 ±.168 .578 ±.173 .561 ±.133 .591 ±.125 .564 ±.106 churn 1 .691 ±.032 .769 ±.027 .758 ±.037 .697 ±.029 .713 ±.003 .717 ±.124 .697 ±.008 .685 ±.024 .758 ±.037 .743 ±.019 .684 ±.029 .639 ±.035 .765 ±.026 .758 ±.037 .765 ±.026 2 .751 ±.052 .777 ±.024 .778 ±.024 .703 ±.015 .725 ±.021 .738 ±.076 .730 ±.031 .695 ±.025 .767 ±.033 .773 ±.025 .695 ±.019 .674 ±.064 .774 ±.019 .764 ±.020 .773 ±.020 3 .780 ±.021 .774 ±.020 .776 ±.025 .712 ±.021 .746 ±.007 .778 ±.023 .747 ±.015 .693 ±.026 .773 ±.032 .777 ±.022 .780 ±.025 .700 ±.091 .779 ±.021 .768 ±.021 .776 ±.023 4 .779 ±.022 .779 ±.022 .780 ±.021 .710 ±.021 .750 ±.011 .779 ±.022 .761 ±.013 .701 ±.018 .779 ±.025 .781 ±.023 .780 ±.025 .734 ±.028 .780 ±.020 .779 ±.022 .782 ±.022 5 .782 ±.023 .779 ±.022 .781 ±.024 .713 ±.019 .783 ±.027 .780 ±.022 .781 ±.020 .701 ±.017 .780 ±.025 .782 ±.023 .780 ±.025 .774 ±.031 .782 ±.022 .778 ±.022 .782 ±.021 aps_failure 1 .958 ±.009 .942 ±.057 .971 ±.007 .877 ±.045 .705 ±.037 .970 ±.005 .869 ±.059 .970 ±.007 .972 ±.007 .970 ±.006 .679 ±.009 .925 ±.013 .727 ±.179 .974 ±.004 .972 ±.006 2 .959 ±.010 .950 ±.047 .971 ±.007 .920 ±.008 .704 ±.047 .973 ±.003 .928 ±.024 .974 ±.005 .974 ±.007 .976 ±.003 .782 ±.037 .939 ±.039 .920 ±.004 .976 ±.005 .973 ±.008 3 .972 ±.016 .978 ±.005 .974 ±.007 .925 ±.003 .911 ±.045 .979 ±.005 .944 ±.027 .978 ±.004 .975 ±.006 .979 ±.004 .974 ±.006 .961 ±.029 .964 ±.012 .979 ±.006 .977 ±.006 4 .982 ±.005 .981 ±.005 .978 ±.007 .981 ±.005 .977 ±.006 .982 ±.005 .967 ±.006 .981 ±.004 .979 ±.005 .981 ±.004 .978 ±.006 .980 ±.004 .966 ±.009 .982 ±.005 .980 ±.004 5 .986 ±.005 .986 ±.005 .985 ±.005 .985 ±.004 .984 ±.005 .986 ±.004 .984 ±.005 .985 ±.004 .985 ±.005 .984 ±.002 .983 ±.006 .985 ±.005 .975 ±.012 .986 ±.005 .984 ±.005 labor negotiations 1 .872 ±.088 .823 ±.122 .921 ±.080 .884 ±.092 .902 ±.092 .788 ±.130 .807 ±.165 .949 ±.058 .934 ±.084 .890 ±.097 .952 ±.082 .808 ±.204 .843 ±.172 .934 ±.084 .900 ±.046 2 .962 ±.055 .943 ±.048 .936 ±.081 .940 ±.074 .948 ±.079 .921 ±.071 .968 ±.045 .960 ±.069 .940 ±.083 .964 ±.043 .948 ±.089 .801 ±.302 .862 ±.175 .968 ±.045 .892 ±.075 3 .968 ±.034 .955 ±.042 .952 ±.063 .947 ±.061 .956 ±.076 .937 ±.072 .960 ±.048 .960 ±.049 .944 ±.065 .943 ±.060 .944 ±.065 .916 ±.113 .943 ±.051 .976 ±.041 .947 ±.017 4 .967 ±.008 .980 ±.018 .968 ±.025 .976 ±.041 .972 ±.048 .972 ±.030 .972 ±.038 .968 ±.036 .972 ±.030 .959 ±.042 .959 ±.042 .972 ±.030 .984 ±.027 .980 ±.034 .967 ±.020 5 .968 ±.025 .976 ±.024 .976 ±.031 .984 ±.018 .972 ±.030 .972 ±.030 .976 ±.031 .980 ±.018 .972 ±.030 .972 ±.030 .972 ±.030 .976 ±.024 .967 ±.030 .972 ±.030 .968 ±.025 kdd_cup_09 appetency 1 .666 ±.083 .524 ±.025 .782 ±.018 .752 ±.024 .539 ±.029 .524 ±.025 .805 ±.024 .785 ±.016 .657 ±.021 .751 ±.025 .684 ±.019 .517 ±.040 .499 ±.002 .751 ±.025 .807 ±.023 2 .743 ±.048 .546 ±.069 .790 ±.018 .752 ±.024 .563 ±.048 .545 ±.069 .808 ±.020 .808 ±.014 .671 ±.019 .782 ±.016 .682 ±.024 .542 ±.005 .533 ±.030 .778 ±.017 .808 ±.020 3 .788 ±.022 .571 ±.048 .798 ±.015 .799 ±.020 .565 ±.047 .571 ±.048 .809 ±.014 .809 ±.014 .737 ±.077 .792 ±.013 .788 ±.013 .627 ±.047 .623 ±.051 .784 ±.015 .797 ±.018 4 .802 ±.007 .593 ±.061 .797 ±.016 .804 ±.022 .610 ±.013 .593 ±.061 .805 ±.012 .800 ±.019 .780 ±.011 .813 ±.013 .802 ±.017 .720 ±.037 .701 ±.039 .783 ±.007 .803 ±.019 5 .796 ±.006 .714 ±.102 .799 ±.005 .797 ±.018 .714 ±.009 .714 ±.102 .798 ±.009 .792 ±.017 .780 ±.010 .797 ±.006 .799 ±.005 .735 ±.036 .738 ±.039 .798 ±.007 .804 ±.013 qsar_oral toxicity 1 .703 ±.013 .693 ±.043 .693 ±.025 .552 ±.057 .556 ±.011 .693 ±.035 .552 ±.051 .551 ±.042 .687 ±.021 .552 ±.057 .534 ±.040 .579 ±.013 .567 ±.014 .692 ±.027 .730 ±.016 2 .745 ±.014 .740 ±.033 .698 ±.032 .598 ±.042 .573 ±.004 .760 ±.017 .605 ±.034 .626 ±.023 .720 ±.022 .598 ±.042 .632 ±.009 .621 ±.052 .629 ±.046 .719 ±.036 .724 ±.025 3 .776 ±.014 .808 ±.037 .758 ±.044 .668 ±.053 .678 ±.023 .820 ±.024 .689 ±.035 .708 ±.052 .761 ±.038 .668 ±.053 .735 ±.029 .693 ±.023 .680 ±.020 .782 ±.043 .751 ±.027 4 .802 ±.019 .833 ±.037 .810 ±.042 .756 ±.014 .781 ±.021 .841 ±.030 .778 ±.018 .776 ±.035 .807 ±.028 .756 ±.014 .801 ±.028 .760 ±.033 .757 ±.011 .828 ±.038 .778 ±.028 5 .828 ±.019 .842 ±.031 .845 ±.028 .824 ±.019 .822 ±.037 .846 ±.024 .818 ±.030 .823 ±.020 .847 ±.020 .824 ±.019 .840 ±.017 .817 ±.026 .809 ±.022 .840 ±.035 .823 ±.027 taiwanese_bankruptcy prediction 1 .922 ±.016 .934 ±.007 .935 ±.005 .932 ±.002 .928 ±.007 .929 ±.021 .918 ±.005 .933 ±.007 .935 ±.005 .924 ±.024 .913 ±.014 .930 ±.010 .925 ±.019 .935 ±.006 .928 ±.010 2 .927 ±.013 .942 ±.002 .936 ±.006 .935 ±.003 .936 ±.012 .941 ±.007 .928 ±.009 .931 ±.015 .933 ±.011 .931 ±.024 .934 ±.004 .941 ±.011 .935 ±.006 .939 ±.002 .940 ±.008 3 .933 ±.014 .942 ±.001 .939 ±.004 .938 ±.005 .939 ±.010 .944 ±.005 .937 ±.013 .933 ±.013 .937 ±.008 .936 ±.016 .938 ±.005 .939 ±.007 .931 ±.014 .938 ±.012 .946 ±.007 4 .937 ±.011 .942 ±.004 .940 ±.008 .936 ±.015 .940 ±.004 .945 ±.007 .938 ±.014 .937 ±.014 .939 ±.009 .937 ±.015 .935 ±.015 .933 ±.017 .932 ±.014 .934 ±.015 .941 ±.006 5 .936 ±.013 .938 ±.016 .937 ±.015 .935 ±.013 .938 ±.010 .937 ±.015 .937 ±.012 .937 ±.015 .937 ±.018 .935 ±.013 .935 ±.013 .939 ±.009 .936 ±.013 .934 ±.014 .939 ±.007 thyroid discordant 1 .825 ±.047 .725 ±.170 .822 ±.046 .810 ±.063 .535 ±.001 .725 ±.170 .823 ±.040 .803 ±.064 .823 ±.054 .836 ±.047 .852 ±.039 .647 ±.101 .822 ±.048 .810 ±.063 .809 ±.065 2 .844 ±.055 .848 ±.050 .844 ±.062 .812 ±.068 .570 ±.005 .848 ±.050 .805 ±.065 .813 ±.067 .842 ±.046 .855 ±.047 .837 ±.055 .747 ±.127 .828 ±.062 .822 ±.080 .826 ±.067 3 .822 ±.093 .855 ±.054 .834 ±.076 .834 ±.076 .560 ±.035 .855 ±.054 .804 ±.082 .815 ±.067 .847 ±.060 .852 ±.052 .828 ±.078 .836 ±.071 .833 ±.062 .819 ±.086 .834 ±.064 4 .832 ±.087 .853 ±.063 .842 ±.074 .839 ±.078 .614 ±.033 .853 ±.063 .816 ±.071 .806 ±.082 .857 ±.052 .858 ±.051 .828 ±.092 .823 ±.079 .839 ±.056 .826 ±.092 .838 ±.068 5 .837 ±.087 .841 ±.083 .841 ±.079 .838 ±.083 .840 ±.080 .841 ±.083 .827 ±.082 .827 ±.077 .839 ±.085 .854 ±.057 .837 ±.084 .831 ±.069 .846 ±.057 .835 ±.085 .851 ±.058 coil_2000 insurance_policies 1 .728 ±.031 .704 ±.049 .727 ±.036 .735 ±.032 .529 ±.010 .664 ±.068 .721 ±.037 .724 ±.043 .732 ±.029 .714 ±.028 .645 ±.029 .694 ±.045 .673 ±.044 .729 ±.030 .725 ±.034 2 .732 ±.029 .717 ±.031 .739 ±.031 .734 ±.036 .565 ±.041 .715 ±.038 .722 ±.036 .731 ±.033 .730 ±.032 .713 ±.030 .653 ±.037 .722 ±.033 .719 ±.042 .732 ±.039 .723 ±.031 3 .739 ±.038 .721 ±.032 .737 ±.034 .732 ±.036 .642 ±.039 .716 ±.034 .719 ±.034 .733 ±.036 .735 ±.026 .716 ±.028 .694 ±.005 .703 ±.024 .699 ±.046 .733 ±.035 .734 ±.033 4 .737 ±.031 .718 ±.034 .740 ±.030 .734 ±.033 .648 ±.018 .716 ±.035 .724 ±.035 .732 ±.028 .734 ±.025 .727 ±.020 .719 ±.028 .738 ±.031 .725 ±.032 .736 ±.031 .730 ±.033 5 .735 ±.027 .724 ±.031 .735 ±.028 .733 ±.031 .650 ±.023 .725 ±.031 .729 ±.034 .728 ±.031 .731 ±.028 .729 ±.025 .718 ±.029 .730 ±.030 .722 ±.041 .735 ±.033 .727 ±.031 58 (continued from previous page) DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCO MarkovBlanket MImRMR(R)ReliefFRandomSBERFImportanceSFS prostate cancer 1 .903 ±.109 .958 ±.063 .954 ±.071 .953 ±.082 .629 ±.040 .939 ±.061 .699 ±.189 .949 ±.060 .948 ±.081 .955 ±.072 .952 ±.059 .640 ±.093 .601 ±.064 .948 ±.060 .908 ±.048 2 .873 ±.124 .963 ±.050 .956 ±.064 .963 ±.058 .700 ±.084 .967 ±.058 .655 ±.169 .933 ±.071 .958 ±.063 .961 ±.065 .956 ±.056 .715 ±.105 .666 ±.188 .964 ±.056 .879 ±.066 3 .931 ±.057 .962 ±.066 .958 ±.072 .958 ±.063 .761 ±.087 .962 ±.054 .868 ±.045 .960 ±.041 .967 ±.055 .958 ±.069 .965 ±.057 .871 ±.019 .859 ±.034 .968 ±.041 .859 ±.063 4 .953 ±.026 .970 ±.043 .963 ±.061 .967 ±.052 .888 ±.035 .961 ±.059 .935 ±.048 .957 ±.044 .957 ±.068 .968 ±.053 .964 ±.062 .941 ±.030 .941 ±.027 .967 ±.041 .956 ±.034 5 .960 ±.058 .970 ±.049 .965 ±.057 .967 ±.044 .946 ±.035 .960 ±.067 .955 ±.042 .973 ±.037 .969 ±.054 .968 ±.053 .973 ±.046 .961 ±.035 .965 ±.054 .962 ±.057 .952 ±.047 credit approval 1 .875 ±.018 .908 ±.016 .906 ±.006 .790 ±.029 .673 ±.079 .907 ±.014 .889 ±.022 .807 ±.013 .906 ±.012 .796 ±.020 .834 ±.011 .757 ±.086 .899 ±.002 .906 ±.007 .905 ±.006 2 .898 ±.007 .915 ±.008 .899 ±.009 .806 ±.009 .767 ±.048 .911 ±.021 .901 ±.012 .807 ±.006 .907 ±.013 .844 ±.056 .852 ±.021 .822 ±.150 .902 ±.023 .902 ±.004 .908 ±.007 3 .899 ±.005 .915 ±.017 .900 ±.009 .822 ±.014 .900 ±.005 .920 ±.013 .907 ±.013 .814 ±.006 .905 ±.005 .876 ±.062 .905 ±.003 .838 ±.065 .907 ±.012 .907 ±.005 .904 ±.011 4 .908 ±.002 .913 ±.014 .918 ±.006 .851 ±.050 .899 ±.002 .913 ±.013 .911 ±.004 .821 ±.015 .911 ±.009 .910 ±.011 .911 ±.005 .843 ±.057 .910 ±.011 .905 ±.005 .910 ±.013 5 .909 ±.004 .912 ±.005 .919 ±.002 .911 ±.004 .907 ±.006 .909 ±.001 .906 ±.008 .882 ±.057 .913 ±.008 .914 ±.005 .918 ±.002 .860 ±.052 .912 ±.007 .911 ±.005 .910 ±.009 heart_disease hungary 1 .800 ±.015 .889 ±.024 .875 ±.029 .829 ±.111 .680 ±.145 .884 ±.023 .756 ±.152 .861 ±.032 .827 ±.081 .860 ±.032 .878 ±.037 .718 ±.143 .834 ±.041 .870 ±.023 .842 ±.036 2 .829 ±.069 .899 ±.010 .895 ±.007 .877 ±.025 .879 ±.052 .898 ±.014 .758 ±.159 .875 ±.025 .887 ±.022 .873 ±.006 .893 ±.012 .855 ±.007 .843 ±.041 .864 ±.020 .861 ±.048 3 .839 ±.040 .887 ±.014 .890 ±.009 .874 ±.030 .864 ±.048 .887 ±.010 .857 ±.057 .878 ±.028 .897 ±.013 .885 ±.012 .892 ±.014 .845 ±.038 .885 ±.027 .875 ±.025 .866 ±.052 4 .877 ±.014 .888 ±.012 .884 ±.014 .873 ±.034 .903 ±.003 .879 ±.012 .879 ±.054 .875 ±.036 .898 ±.025 .900 ±.006 .889 ±.014 .872 ±.004 .898 ±.011 .883 ±.011 .905 ±.007 5 .881 ±.011 .898 ±.007 .891 ±.014 .885 ±.010 .898 ±.007 .887 ±.020 .869 ±.049 .887 ±.032 .896 ±.011 .897 ±.006 .886 ±.010 .881 ±.022 .900 ±.007 .888 ±.015 .900 ±.007 give_me some_credit 1 .573 ±.004 .732 ±.001 .732 ±.005 .732 ±.005 .582 ±.017 .732 ±.001 .756 ±.001 .733 ±.005 .759 ±.003 .732 ±.005 .641 ±.006 .693 ±.025 .751 ±.012 .573 ±.004 .759 ±.003 2 .741 ±.003 .801 ±.016 .810 ±.003 .774 ±.003 .690 ±.011 .801 ±.016 .791 ±.020 .774 ±.003 .794 ±.003 .806 ±.009 .675 ±.002 .772 ±.020 .768 ±.030 .648 ±.007 .782 ±.014 3 .742 ±.003 .819 ±.005 .822 ±.003 .774 ±.003 .724 ±.038 .819 ±.005 .802 ±.016 .776 ±.003 .822 ±.003 .822 ±.003 .774 ±.003 .749 ±.029 .802 ±.003 .742 ±.003 .787 ±.004 4 .793 ±.016 .823 ±.004 .824 ±.003 .775 ±.004 .785 ±.033 .823 ±.004 .817 ±.004 .776 ±.005 .824 ±.003 .823 ±.004 .804 ±.024 .796 ±.020 .816 ±.004 .812 ±.011 .811 ±.012 5 .817 ±.004 .825 ±.003 .824 ±.004 .808 ±.010 .814 ±.015 .825 ±.003 .817 ±.004 .804 ±.004 .824 ±.003 .825 ±.003 .824 ±.004 .821 ±.007 .817 ±.004 .817 ±.004 .818 ±.012 clock_protein toxicity 1 .496 ±.104 .457 ±.034 .443 ±.042 .527 ±.119 .482 ±.060 .457 ±.034 .463 ±.115 .455 ±.069 .493 ±.089 .550 ±.125 .432 ±.062 .457 ±.047 .447 ±.045 .508 ±.116 .512 ±.123 2 .486 ±.038 .509 ±.053 .442 ±.036 .544 ±.079 .424 ±.091 .509 ±.053 .439 ±.027 .465 ±.076 .492 ±.116 .525 ±.090 .391 ±.095 .497 ±.053 .553 ±.118 .581 ±.087 .491 ±.073 3 .473 ±.059 .548 ±.089 .499 ±.011 .554 ±.038 .454 ±.041 .548 ±.089 .449 ±.030 .444 ±.030 .473 ±.101 .457 ±.070 .355 ±.115 .481 ±.080 .448 ±.015 .511 ±.083 .525 ±.105 4 .487 ±.095 .473 ±.105 .477 ±.064 .480 ±.030 .500 ±.100 .473 ±.105 .491 ±.031 .464 ±.054 .479 ±.048 .473 ±.047 .489 ±.047 .511 ±.088 .519 ±.099 .522 ±.024 .516 ±.037 5 .440 ±.056 .438 ±.054 .478 ±.055 .459 ±.040 .512 ±.082 .438 ±.054 .455 ±.021 .447 ±.014 .491 ±.039 .461 ±.053 .479 ±.070 .497 ±.110 .489 ±.071 .447 ±.071 .470 ±.106 online_shoppers_purchasing intention_dataset 1 .877 ±.010 .876 ±.010 .876 ±.010 .876 ±.010 .679 ±.012 .876 ±.010 .909 ±.003 .883 ±.026 .874 ±.011 .911 ±.005 .708 ±.033 .691 ±.019 .912 ±.008 .876 ±.010 .882 ±.009 2 .872 ±.009 .912 ±.006 .879 ±.012 .886 ±.027 .708 ±.015 .911 ±.006 .911 ±.005 .913 ±.007 .884 ±.025 .915 ±.007 .854 ±.100 .699 ±.025 .913 ±.002 .872 ±.009 .911 ±.005 3 .908 ±.005 .910 ±.006 .912 ±.007 .910 ±.007 .737 ±.008 .910 ±.006 .911 ±.004 .911 ±.008 .911 ±.005 .913 ±.007 .913 ±.006 .885 ±.012 .912 ±.004 .909 ±.007 .912 ±.005 4 .910 ±.005 .911 ±.006 .910 ±.005 .910 ±.005 .910 ±.005 .911 ±.006 .910 ±.004 .909 ±.005 .911 ±.005 .914 ±.008 .912 ±.008 .905 ±.010 .912 ±.004 .910 ±.004 .911 ±.004 5 .910 ±.004 .910 ±.004 .910 ±.006 .909 ±.004 .909 ±.005 .910 ±.004 .911 ±.004 .910 ±.005 .911 ±.004 .913 ±.006 .912 ±.007 .844 ±.090 .911 ±.004 .910 ±.004 .910 ±.004 credit_card clients_default 1 .710 ±.003 .713 ±.004 .703 ±.004 .703 ±.004 .659 ±.002 .711 ±.005 .719 ±.006 .720 ±.010 .702 ±.004 .715 ±.003 .715 ±.004 .651 ±.028 .719 ±.009 .714 ±.005 .710 ±.008 2 .726 ±.002 .732 ±.006 .732 ±.004 .735 ±.003 .677 ±.009 .727 ±.007 .728 ±.008 .735 ±.003 .730 ±.004 .723 ±.004 .725 ±.002 .710 ±.027 .729 ±.004 .721 ±.004 .722 ±.014 3 .734 ±.004 .737 ±.004 .740 ±.003 .740 ±.005 .711 ±.021 .736 ±.003 .739 ±.004 .738 ±.003 .740 ±.004 .740 ±.005 .728 ±.002 .729 ±.008 .735 ±.005 .732 ±.002 .738 ±.002 4 .741 ±.004 .741 ±.002 .744 ±.003 .743 ±.002 .728 ±.004 .740 ±.001 .742 ±.002 .741 ±.004 .741 ±.004 .744 ±.003 .731 ±.003 .735 ±.007 .742 ±.003 .741 ±.004 .742 ±.002 5 .745 ±.002 .745 ±.001 .746 ±.003 .745 ±.003 .743 ±.004 .745 ±.001 .743 ±.003 .744 ±.004 .743 ±.003 .746 ±.002 .742 ±.002 .738 ±.010 .745 ±.001 .744 ±.004 .744 ±.001 bioresponse 1 .785 ±.017 .760 ±.008 .773 ±.003 .670 ±.018 .506 ±.002 .762 ±.006 .573 ±.044 .709 ±.021 .775 ±.015 .708 ±.036 .756 ±.010 .530 ±.010 .539 ±.014 .777 ±.020 .768 ±.025 2 .783 ±.021 .794 ±.019 .789 ±.018 .720 ±.026 .507 ±.010 .801 ±.012 .623 ±.047 .748 ±.029 .793 ±.010 .734 ±.017 .772 ±.016 .693 ±.066 .639 ±.044 .796 ±.017 .777 ±.019 3 .797 ±.021 .818 ±.014 .803 ±.021 .745 ±.027 .511 ±.009 .816 ±.012 .713 ±.025 .775 ±.024 .802 ±.015 .744 ±.023 .788 ±.015 .679 ±.031 .657 ±.009 .800 ±.022 .786 ±.009 4 .819 ±.011 .824 ±.014 .813 ±.021 .792 ±.017 .517 ±.007 .819 ±.015 .747 ±.032 .802 ±.016 .808 ±.018 .772 ±.020 .802 ±.012 .729 ±.035 .735 ±.024 .807 ±.018 .788 ±.017 5 .803 ±.010 .797 ±.015 .801 ±.006 .792 ±.016 .711 ±.034 .803 ±.004 .786 ±.017 .796 ±.021 .802 ±.010 .792 ±.014 .800 ±.022 .785 ±.019 .782 ±.028 .807 ±.018 .787 ±.025 lymphography 1 .888 ±.032 .827 ±.113 .857 ±.048 .828 ±.034 .625 ±.069 .839 ±.121 .833 ±.119 .837 ±.045 .874 ±.073 .901 ±.043 .879 ±.085 .759 ±.135 .836 ±.099 .892 ±.050 .846 ±.082 2 .940 ±.010 .840 ±.116 .883 ±.064 .906 ±.004 .710 ±.145 .846 ±.122 .895 ±.062 .883 ±.012 .899 ±.057 .891 ±.053 .898 ±.074 .753 ±.224 .890 ±.035 .933 ±.036 .874 ±.044 3 .939 ±.029 .862 ±.104 .872 ±.051 .919 ±.021 .848 ±.119 .855 ±.100 .894 ±.093 .888 ±.008 .929 ±.033 .881 ±.026 .888 ±.053 .873 ±.020 .908 ±.055 .935 ±.029 .854 ±.087 4 .918 ±.019 .889 ±.077 .878 ±.032 .917 ±.031 .872 ±.101 .870 ±.065 .882 ±.086 .898 ±.027 .908 ±.040 .871 ±.057 .906 ±.048 .865 ±.064 .904 ±.068 .919 ±.033 .860 ±.096 5 .910 ±.060 .893 ±.084 .909 ±.061 .905 ±.056 .873 ±.097 .873 ±.074 .885 ±.080 .894 ±.060 .896 ±.034 .883 ±.105 .923 ±.050 .903 ±.081 .890 ±.079 .920 ±.057 .880 ±.110 nci_ovarian cancer 1 .994 ±.005 .990 ±.012 .990 ±.013 .991 ±.015 .658 ±.148 1.00 ±.00 .812 ±.080 .981 ±.033 .990 ±.014 1.00 ±.00 .990 ±.013 .782 ±.217 .815 ±.111 .996 ±.007 .993 ±.005 2 .998 ±.004 1.00 ±.00 1.00 ±.00 .999 ±.001 .884 ±.064 1.00 ±.00 .971 ±.009 1.00 ±.00 .999 ±.001 1.00 ±.00 .998 ±.003 .981 ±.021 .984 ±.024 1.00 ±.00 1.00 ±.00 3 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 .957 ±.045 1.00 ±.00 .998 ±.003 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 .998 ±.004 .999 ±.001 1.00 ±.00 1.00 ±.00 4 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 .998 ±.002 1.00 ±.00 .999 ±.002 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 5 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 .999 ±.002 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 bad_customer detection 1 .607 ±.030 .602 ±.031 .630 ±.024 .718 ±.034 .666 ±.048 .602 ±.031 .649 ±.050 .659 ±.069 .697 ±.038 .689 ±.018 .612 ±.036 .652 ±.025 .695 ±.018 .607 ±.030 .706 ±.009 2 .696 ±.024 .627 ±.016 .664 ±.011 .717 ±.040 .667 ±.050 .627 ±.016 .654 ±.044 .698 ±.057 .724 ±.038 .705 ±.025 .632 ±.017 .579 ±.046 .702 ±.021 .696 ±.024 .715 ±.008 3 .708 ±.037 .657 ±.010 .662 ±.009 .720 ±.041 .667 ±.028 .657 ±.010 .699 ±.035 .719 ±.036 .727 ±.038 .714 ±.044 .630 ±.008 .689 ±.069 .707 ±.027 .703 ±.029 .712 ±.019 4 .721 ±.041 .666 ±.029 .672 ±.011 .717 ±.036 .680 ±.016 .666 ±.029 .703 ±.033 .717 ±.039 .720 ±.041 .714 ±.036 .652 ±.012 .688 ±.015 .711 ±.018 .717 ±.044 .707 ±.021 5 .717 ±.045 .712 ±.036 .700 ±.039 .730 ±.033 .681 ±.012 .712 ±.036 .715 ±.038 .721 ±.044 .720 ±.040 .718 ±.042 .667 ±.009 .720 ±.042 .716 ±.037 .727 ±.034 .715 ±.038 hazelnut_spread contaminant_detection_10GHz 1 .898 ±.020 .881 ±.014 .893 ±.016 .888 ±.008 .724 ±.024 .881 ±.014 .845 ±.027 .879 ±.024 .885 ±.009 .883 ±.003 .887 ±.011 .743 ±.041 .861 ±.036 .896 ±.020 .859 ±.036 2 .920 ±.020 .912 ±.011 .909 ±.013 .917 ±.013 .793 ±.014 .912 ±.011 .877 ±.012 .909 ±.021 .917 ±.012 .907 ±.003 .908 ±.008 .831 ±.063 .884 ±.025 .918 ±.018 .895 ±.029 3 .928 ±.017 .930 ±.008 .922 ±.008 .928 ±.018 .838 ±.011 .930 ±.008 .909 ±.021 .930 ±.015 .926 ±.015 .923 ±.009 .931 ±.008 .895 ±.041 .913 ±.025 .936 ±.013 .922 ±.022 4 .937 ±.012 .945 ±.011 .928 ±.010 .936 ±.013 .883 ±.012 .945 ±.011 .927 ±.021 .934 ±.010 .937 ±.013 .933 ±.011 .939 ±.012 .915 ±.025 .937 ±.010 .942 ±.011 .927 ±.022 5 .945 ±.013 .950 ±.010 .932 ±.012 .941 ±.013 .917 ±.010 .950 ±.010 .939 ±.010 .943 ±.012 .940 ±.014 .948 ±.009 .944 ±.011 .935 ±.023 .945 ±.009 .945 ±.012 .937 ±.015 drug_induced autoimmunity_prediction 1 .615 ±.067 .598 ±.106 .587 ±.012 .627 ±.024 .545 ±.074 .614 ±.040 .539 ±.075 .573 ±.008 .564 ±.033 .538 ±.030 .550 ±.040 .541 ±.072 .526 ±.059 .584 ±.055 .513 ±.059 2 .614 ±.094 .596 ±.083 .619 ±.043 .648 ±.037 .591 ±.043 .601 ±.039 .568 ±.091 .580 ±.031 .565 ±.048 .589 ±.042 .564 ±.088 .535 ±.004 .573 ±.033 .593 ±.029 .645 ±.033 3 .668 ±.079 .649 ±.059 .606 ±.043 .644 ±.046 .612 ±.017 .629 ±.021 .633 ±.005 .636 ±.048 .636 ±.017 .617 ±.007 .629 ±.079 .590 ±.018 .547 ±.079 .645 ±.039 .616 ±.052 4 .686 ±.063 .673 ±.021 .642 ±.032 .677 ±.037 .630 ±.056 .687 ±.019 .662 ±.026 .694 ±.050 .677 ±.013 .660 ±.033 .669 ±.025 .664 ±.029 .650 ±.029 .690 ±.034 .675 ±.026 5 .725 ±.042 .706 ±.064 .696 ±.031 .711 ±.012 .682 ±.035 .697 ±.039 .708 ±.012 .712 ±.027 .707 ±.013 .636 ±.025 .714 ±.020 .675 ±.025 .652 ±.013 .708 ±.019 .681 ±.085 heart_disease switzerland 1 .608 ±.256 .373 ±.191 .794 ±.224 .715 ±.058 .725 ±.157 .373 ±.191 .608 ±.268 .633 ±.290 .687 ±.134 .654 ±.112 .896 ±.068 .532 ±.317 .510 ±.175 .463 ±.215 .464 ±.124 2 .733 ±.280 .418 ±.223 .784 ±.168 .709 ±.143 .738 ±.154 .418 ±.223 .599 ±.245 .638 ±.231 .908 ±.057 .829 ±.027 .860 ±.130 .657 ±.257 .665 ±.295 .697 ±.196 .448 ±.138 3 .787 ±.178 .467 ±.218 .775 ±.180 .568 ±.266 .741 ±.116 .467 ±.218 .701 ±.165 .535 ±.288 .817 ±.179 .854 ±.102 .794 ±.172 .685 ±.233 .619 ±.325 .676 ±.238 .667 ±.291 4 .743 ±.179 .659 ±.221 .690 ±.247 .574 ±.281 .564 ±.278 .659 ±.221 .791 ±.205 .541 ±.276 .810 ±.172 .856 ±.100 .682 ±.232 .603 ±.331 .691 ±.226 .727 ±.204 .752 ±.194 5 .723 ±.196 .667 ±.256 .717 ±.227 .627 ±.235 .700 ±.174 .667 ±.256 .733 ±.198 .575 ±.336 .698 ±.231 .768 ±.233 .709 ±.221 .679 ±.240 .712 ±.219 .701 ±.226 .706 ±.224 59 (continued from previous page) DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCO MarkovBlanket MImRMR(R)ReliefFRandomSBERFImportanceSFS hr analytics 1 .685 ±.004 .751 ±.007 .751 ±.007 .727 ±.018 .644 ±.005 .751 ±.007 .688 ±.004 .751 ±.007 .720 ±.005 .751 ±.007 .694 ±.018 .579 ±.045 .759 ±.021 .685 ±.004 .746 ±.006 2 .752 ±.006 .763 ±.007 .752 ±.009 .781 ±.007 .665 ±.001 .763 ±.007 .689 ±.003 .784 ±.007 .784 ±.007 .763 ±.006 .739 ±.019 .703 ±.004 .770 ±.016 .727 ±.002 .773 ±.016 3 .787 ±.006 .773 ±.012 .770 ±.007 .786 ±.007 .689 ±.020 .773 ±.012 .721 ±.038 .789 ±.002 .786 ±.007 .764 ±.007 .770 ±.008 .774 ±.020 .775 ±.014 .787 ±.006 .785 ±.010 4 .793 ±.004 .774 ±.012 .776 ±.012 .794 ±.004 .699 ±.013 .774 ±.012 .751 ±.039 .794 ±.004 .795 ±.004 .766 ±.005 .776 ±.011 .788 ±.007 .777 ±.013 .791 ±.003 .788 ±.012 5 .794 ±.004 .788 ±.011 .777 ±.012 .796 ±.004 .770 ±.007 .788 ±.011 .793 ±.010 .796 ±.004 .796 ±.005 .770 ±.005 .777 ±.012 .794 ±.006 .795 ±.005 .794 ±.005 .790 ±.013 porto seguro 1 .595 ±.002 .612 ±.005 .599 ±.003 .606 ±.002 .550 ±.003 .600 ±.015 .590 ±.017 .606 ±.006 .590 ±.002 .601 ±.006 .586 ±.001 .588 ±.006 .581 ±.016 .594 ±.002 .583 ±.003 2 .605 ±.002 .621 ±.003 .616 ±.007 .616 ±.002 .597 ±.003 .607 ±.013 .605 ±.009 .619 ±.002 .597 ±.002 .615 ±.003 .597 ±.003 .608 ±.002 .610 ±.008 .605 ±.002 .596 ±.004 3 .612 ±.002 .625 ±.004 .626 ±.003 .623 ±.001 .609 ±.003 .614 ±.009 .613 ±.012 .623 ±.002 .610 ±.002 .618 ±.003 .608 ±.004 .612 ±.006 .616 ±.005 .622 ±.002 .614 ±.003 4 .628 ±.001 .627 ±.003 .629 ±.002 .625 ±.003 .621 ±.003 .619 ±.009 .618 ±.012 .626 ±.003 .627 ±.001 .623 ±.002 .615 ±.001 .618 ±.005 .621 ±.007 .627 ±.002 .623 ±.003 5 .630 ±.001 .628 ±.003 .631 ±.001 .629 ±.000 .626 ±.001 .624 ±.008 .626 ±.003 .629 ±.003 .630 ±.001 .629 ±.002 .627 ±.001 .629 ±.002 .626 ±.005 .631 ±.000 .625 ±.003 japanese_credit screening 1 .812 ±.079 .747 ±.167 .838 ±.095 .836 ±.105 .687 ±.143 .828 ±.066 .677 ±.087 .833 ±.099 .821 ±.045 .828 ±.113 .746 ±.112 .514 ±.108 .587 ±.251 .837 ±.095 .586 ±.251 2 .817 ±.071 .791 ±.106 .776 ±.104 .801 ±.059 .731 ±.136 .784 ±.116 .651 ±.132 .813 ±.047 .817 ±.045 .797 ±.172 .828 ±.049 .605 ±.062 .686 ±.133 .827 ±.058 .689 ±.147 3 .805 ±.075 .806 ±.095 .775 ±.123 .796 ±.077 .726 ±.140 .796 ±.079 .673 ±.151 .800 ±.075 .805 ±.043 .770 ±.122 .791 ±.057 .673 ±.096 .688 ±.145 .826 ±.052 .786 ±.033 4 .802 ±.059 .799 ±.063 .783 ±.111 .797 ±.050 .711 ±.073 .803 ±.063 .758 ±.043 .809 ±.071 .805 ±.044 .803 ±.138 .810 ±.041 .800 ±.069 .747 ±.046 .806 ±.043 .754 ±.052 5 .803 ±.056 .802 ±.055 .785 ±.075 .801 ±.048 .808 ±.069 .801 ±.055 .760 ±.049 .815 ±.066 .798 ±.051 .828 ±.078 .804 ±.052 .820 ±.036 .760 ±.049 .801 ±.047 .760 ±.049 ionosphere 1 .809 ±.071 .830 ±.037 .793 ±.035 .769 ±.070 .593 ±.077 .778 ±.017 .794 ±.057 .783 ±.048 .676 ±.056 .775 ±.038 .692 ±.086 .660 ±.094 .754 ±.093 .809 ±.034 .807 ±.050 2 .831 ±.038 .861 ±.039 .786 ±.011 .822 ±.043 .832 ±.051 .865 ±.019 .818 ±.055 .844 ±.046 .785 ±.086 .852 ±.093 .801 ±.062 .819 ±.073 .841 ±.076 .852 ±.050 .864 ±.051 3 .857 ±.049 .885 ±.039 .840 ±.032 .887 ±.056 .846 ±.047 .884 ±.029 .812 ±.033 .887 ±.063 .850 ±.039 .894 ±.067 .857 ±.046 .796 ±.056 .857 ±.080 .863 ±.041 .882 ±.049 4 .878 ±.056 .908 ±.008 .856 ±.023 .882 ±.062 .867 ±.050 .905 ±.001 .908 ±.034 .866 ±.037 .854 ±.041 .920 ±.040 .860 ±.026 .897 ±.020 .885 ±.063 .883 ±.030 .899 ±.059 5 .890 ±.072 .891 ±.037 .884 ±.054 .866 ±.052 .864 ±.056 .912 ±.007 .880 ±.032 .878 ±.050 .882 ±.058 .912 ±.040 .873 ±.046 .904 ±.044 .881 ±.032 .898 ±.041 .916 ±.039 smoking lung_cancer 1 .683 ±.064 .671 ±.068 .688 ±.048 .697 ±.069 .639 ±.041 .635 ±.035 .610 ±.060 .682 ±.086 .677 ±.079 .698 ±.071 .686 ±.013 .577 ±.063 .525 ±.038 .742 ±.039 .562 ±.046 2 .755 ±.031 .735 ±.047 .696 ±.050 .705 ±.046 .687 ±.043 .734 ±.077 .616 ±.034 .716 ±.016 .692 ±.055 .690 ±.066 .709 ±.023 .585 ±.065 .552 ±.057 .703 ±.074 .659 ±.033 3 .718 ±.026 .795 ±.045 .694 ±.022 .717 ±.020 .680 ±.072 .765 ±.079 .719 ±.021 .713 ±.020 .719 ±.097 .717 ±.039 .714 ±.021 .654 ±.073 .656 ±.060 .723 ±.053 .707 ±.011 4 .729 ±.066 .759 ±.034 .744 ±.035 .747 ±.041 .757 ±.101 .776 ±.038 .738 ±.087 .742 ±.057 .731 ±.064 .746 ±.048 .757 ±.049 .742 ±.072 .777 ±.071 .764 ±.035 .774 ±.066 5 .757 ±.073 .762 ±.057 .751 ±.054 .756 ±.040 .784 ±.072 .778 ±.056 .779 ±.051 .765 ±.051 .765 ±.040 .767 ±.066 .769 ±.074 .758 ±.070 .764 ±.067 .764 ±.065 .762 ±.068 nci_pancreatic cancer 1 .658 ±.076 .694 ±.084 .644 ±.061 .614 ±.073 .583 ±.071 .572 ±.054 .616 ±.077 .588 ±.091 .519 ±.047 .537 ±.097 .657 ±.026 .541 ±.124 .559 ±.081 .637 ±.074 .547 ±.018 2 .669 ±.084 .754 ±.081 .667 ±.088 .619 ±.073 .538 ±.064 .731 ±.098 .619 ±.081 .672 ±.066 .541 ±.058 .689 ±.074 .697 ±.096 .644 ±.029 .554 ±.061 .680 ±.065 .651 ±.025 3 .679 ±.079 .767 ±.082 .711 ±.083 .676 ±.093 .570 ±.048 .757 ±.071 .665 ±.083 .624 ±.059 .627 ±.160 .698 ±.061 .712 ±.093 .704 ±.079 .678 ±.102 .748 ±.082 .663 ±.047 4 .701 ±.086 .751 ±.080 .725 ±.067 .703 ±.089 .623 ±.064 .758 ±.067 .687 ±.124 .659 ±.027 .670 ±.105 .716 ±.091 .723 ±.099 .692 ±.053 .685 ±.058 .740 ±.071 .680 ±.074 5 .710 ±.075 .755 ±.070 .755 ±.049 .687 ±.080 .647 ±.093 .730 ±.059 .726 ±.091 .692 ±.048 .721 ±.092 .728 ±.086 .734 ±.082 .725 ±.058 .720 ±.076 .756 ±.064 .714 ±.114 homesite_quote conversion 1 .873 ±.001 .784 ±.002 .837 ±.001 .600 ±.135 .502 ±.000 .784 ±.002 .746 ±.108 .750 ±.025 .809 ±.002 .777 ±.003 .784 ±.006 .588 ±.035 .593 ±.018 .780 ±.005 .778 ±.003 2 .894 ±.010 .910 ±.001 .839 ±.001 .782 ±.004 .609 ±.090 .910 ±.001 .812 ±.010 .820 ±.003 .836 ±.001 .787 ±.003 .843 ±.049 .687 ±.052 .682 ±.043 .893 ±.001 .788 ±.013 3 .925 ±.003 .929 ±.002 .859 ±.001 .849 ±.001 .667 ±.001 .927 ±.001 .837 ±.013 .866 ±.002 .877 ±.002 .829 ±.018 .916 ±.003 .738 ±.046 .737 ±.036 .929 ±.002 .824 ±.017 4 .936 ±.001 .939 ±.001 .923 ±.001 .878 ±.002 .680 ±.001 .938 ±.001 .861 ±.017 .873 ±.006 .899 ±.001 .883 ±.012 .924 ±.003 .756 ±.025 .765 ±.014 .935 ±.001 .836 ±.032 5 .940 ±.001 .942 ±.001 .934 ±.000 .893 ±.002 .728 ±.006 .942 ±.001 .875 ±.013 .892 ±.003 .939 ±.001 .940 ±.000 .934 ±.001 .869 ±.020 .860 ±.013 .938 ±.001 .900 ±.010 heart_disease va_long_beach 1 .590 ±.039 .629 ±.138 .681 ±.102 .652 ±.089 .708 ±.101 .629 ±.138 .644 ±.098 .679 ±.076 .664 ±.124 .630 ±.068 .622 ±.037 .632 ±.150 .652 ±.092 .560 ±.055 .654 ±.125 2 .615 ±.081 .646 ±.145 .693 ±.072 .664 ±.080 .677 ±.126 .646 ±.145 .660 ±.122 .662 ±.074 .663 ±.085 .603 ±.071 .623 ±.087 .690 ±.049 .676 ±.140 .604 ±.094 .668 ±.128 3 .625 ±.097 .652 ±.149 .685 ±.085 .659 ±.050 .678 ±.127 .652 ±.149 .645 ±.083 .677 ±.088 .687 ±.101 .620 ±.092 .652 ±.058 .684 ±.107 .680 ±.115 .670 ±.087 .638 ±.129 4 .675 ±.102 .638 ±.140 .688 ±.092 .680 ±.090 .677 ±.128 .638 ±.140 .662 ±.102 .662 ±.074 .669 ±.083 .623 ±.092 .663 ±.085 .658 ±.108 .686 ±.125 .697 ±.107 .654 ±.111 5 .671 ±.120 .631 ±.115 .678 ±.105 .676 ±.099 .674 ±.107 .631 ±.115 .669 ±.126 .663 ±.101 .672 ±.096 .673 ±.096 .670 ±.089 .663 ±.116 .679 ±.135 .663 ±.101 .670 ±.130 qsar_biodeg 1 .862 ±.005 .856 ±.022 .824 ±.009 .866 ±.013 .680 ±.059 .840 ±.025 .838 ±.046 .885 ±.002 .845 ±.029 .879 ±.011 .874 ±.017 .811 ±.070 .875 ±.029 .820 ±.008 .865 ±.008 2 .885 ±.009 .902 ±.004 .879 ±.008 .881 ±.008 .846 ±.006 .885 ±.019 .886 ±.014 .891 ±.010 .897 ±.011 .884 ±.009 .897 ±.012 .880 ±.008 .898 ±.021 .887 ±.011 .880 ±.014 3 .896 ±.006 .905 ±.005 .905 ±.002 .899 ±.013 .898 ±.005 .898 ±.005 .904 ±.005 .903 ±.002 .901 ±.011 .892 ±.012 .897 ±.011 .908 ±.007 .902 ±.003 .899 ±.012 .896 ±.015 4 .903 ±.010 .907 ±.007 .910 ±.003 .905 ±.002 .907 ±.003 .908 ±.003 .907 ±.004 .906 ±.001 .905 ±.010 .903 ±.008 .904 ±.005 .904 ±.014 .905 ±.004 .900 ±.004 .905 ±.005 5 .904 ±.004 .909 ±.005 .910 ±.005 .907 ±.002 .910 ±.005 .909 ±.005 .909 ±.002 .910 ±.003 .904 ±.006 .910 ±.007 .908 ±.004 .907 ±.008 .909 ±.002 .910 ±.003 .908 ±.004 naticusdroid_android permissions_dataset 1 .974 ±.002 .975 ±.003 .973 ±.002 .848 ±.049 .579 ±.002 .969 ±.010 .969 ±.005 .775 ±.095 .970 ±.007 .848 ±.049 .969 ±.003 .877 ±.071 .820 ±.038 .974 ±.002 .970 ±.006 2 .978 ±.002 .980 ±.003 .976 ±.002 .931 ±.001 .633 ±.003 .978 ±.005 .974 ±.005 .868 ±.044 .976 ±.002 .931 ±.001 .975 ±.002 .877 ±.011 .895 ±.071 .978 ±.002 .972 ±.005 3 .978 ±.002 .981 ±.003 .980 ±.003 .951 ±.009 .722 ±.010 .980 ±.003 .976 ±.004 .947 ±.007 .979 ±.003 .951 ±.009 .977 ±.002 .945 ±.010 .926 ±.041 .979 ±.002 .976 ±.003 4 .980 ±.002 .981 ±.003 .980 ±.003 .973 ±.004 .820 ±.001 .981 ±.002 .977 ±.004 .961 ±.001 .980 ±.004 .973 ±.004 .979 ±.002 .949 ±.029 .968 ±.013 .980 ±.002 .978 ±.004 5 .981 ±.002 .981 ±.003 .981 ±.003 .979 ±.001 .951 ±.008 .981 ±.002 .978 ±.004 .977 ±.004 .980 ±.002 .979 ±.001 .981 ±.002 .968 ±.009 .978 ±.004 .981 ±.002 .979 ±.004 framingham heart_study 1 .641 ±.069 .713 ±.029 .700 ±.021 .698 ±.022 .563 ±.019 .704 ±.025 .701 ±.028 .689 ±.037 .700 ±.021 .700 ±.013 .690 ±.022 .598 ±.033 .698 ±.014 .662 ±.044 .692 ±.015 2 .678 ±.033 .717 ±.028 .698 ±.021 .698 ±.021 .593 ±.016 .717 ±.028 .713 ±.035 .689 ±.034 .701 ±.018 .715 ±.023 .702 ±.032 .660 ±.018 .714 ±.027 .697 ±.020 .699 ±.012 3 .707 ±.032 .716 ±.029 .716 ±.023 .709 ±.026 .668 ±.012 .717 ±.029 .720 ±.029 .709 ±.027 .702 ±.020 .718 ±.023 .708 ±.031 .683 ±.028 .716 ±.029 .707 ±.032 .720 ±.018 4 .713 ±.030 .717 ±.028 .717 ±.023 .705 ±.030 .722 ±.022 .717 ±.028 .718 ±.030 .705 ±.030 .705 ±.025 .717 ±.020 .707 ±.032 .717 ±.033 .716 ±.030 .715 ±.031 .721 ±.018 5 .713 ±.027 .717 ±.028 .721 ±.025 .715 ±.025 .719 ±.025 .716 ±.028 .717 ±.030 .713 ±.027 .715 ±.024 .715 ±.025 .714 ±.025 .693 ±.038 .716 ±.026 .714 ±.026 .719 ±.025 Table D.7: Per-cell results with downstream model RF. Tasks are grouped by metric. Each cell is the mean over three seeds, with the sample standard deviation as a subscript. DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCO MarkovBlanket MImRMR(R)ReliefFRandomSBERFImportanceSFS log-loss (↓) 60 (continued from previous page) DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCO MarkovBlanket MImRMR(R)ReliefFRandomSBERFImportanceSFS horse_colic survival 1 1.02 ±.11 .939 ±.184 .927 ±.080 .982 ±.177 1.12 ±.27 .945 ±.177 .961 ±.101 .862 ±.049 .915 ±.123 1.02 ±.11 1.06 ±.27 1.32 ±.60 1.17 ±.29 .913 ±.021 1.44 ±.16 2 .796 ±.047 .754 ±.063 .769 ±.035 .794 ±.113 .947 ±.151 .741 ±.027 .816 ±.100 .727 ±.011 .803 ±.135 .773 ±.031 .780 ±.043 .847 ±.121 .854 ±.160 .747 ±.008 .903 ±.157 3 .751 ±.026 .754 ±.043 .722 ±.026 .751 ±.081 .818 ±.107 .751 ±.030 .753 ±.093 .728 ±.024 .762 ±.099 .728 ±.044 .762 ±.003 .740 ±.037 .764 ±.016 .734 ±.014 .812 ±.166 4 .722 ±.012 .724 ±.038 .722 ±.024 .708 ±.015 .725 ±.061 .723 ±.036 .741 ±.093 .719 ±.021 .712 ±.046 .726 ±.023 .721 ±.018 .765 ±.052 .699 ±.027 .722 ±.030 .783 ±.068 5 .717 ±.015 .712 ±.027 .723 ±.030 .721 ±.013 .716 ±.032 .731 ±.051 .744 ±.095 .712 ±.026 .718 ±.029 .718 ±.022 .718 ±.027 .743 ±.082 .709 ±.046 .721 ±.031 .772 ±.075 srbct prediction 1 .653 ±.194 .274 ±.094 .486 ±.184 .517 ±.213 1.38 ±.14 .362 ±.205 1.30 ±.18 .557 ±.095 .782 ±.412 .630 ±.372 .363 ±.112 1.08 ±.65 1.03 ±.38 .486 ±.132 1.18 ±.27 2 .610 ±.090 .265 ±.031 .265 ±.058 .316 ±.052 1.01 ±.04 .256 ±.043 .812 ±.124 .329 ±.107 .307 ±.094 .320 ±.072 .285 ±.028 1.07 ±.08 .884 ±.115 .260 ±.037 .883 ±.174 3 .602 ±.015 .267 ±.033 .277 ±.040 .287 ±.057 .861 ±.020 .281 ±.043 .794 ±.073 .309 ±.049 .306 ±.046 .285 ±.062 .271 ±.034 .702 ±.031 .765 ±.072 .285 ±.023 .687 ±.053 4 .627 ±.017 .331 ±.015 .327 ±.032 .323 ±.029 .689 ±.045 .342 ±.016 .621 ±.043 .314 ±.037 .315 ±.033 .324 ±.017 .332 ±.027 .646 ±.034 .705 ±.125 .332 ±.026 .647 ±.048 5 .563 ±.056 .401 ±.009 .392 ±.012 .400 ±.014 .563 ±.011 .437 ±.005 .566 ±.059 .399 ±.022 .404 ±.018 .408 ±.019 .392 ±.010 .543 ±.046 .539 ±.060 .411 ±.016 .566 ±.025 sdss_17 1 .164 ±.004 .189 ±.023 .221 ±.003 .225 ±.006 .776 ±.003 .356 ±.331 .165 ±.003 .165 ±.003 .203 ±.002 .452 ±.002 .250 ±.004 .715 ±.527 .166 ±.004 .225 ±.006 .203 ±.002 2 .0923 ±.0042 .0938 ±.0056 .105 ±.006 .0948 ±.0049 .504 ±.139 .0919 ±.0053 .0911 ±.0031 .0961 ±.0108 .0930 ±.0028 .173 ±.004 .105 ±.005 .116 ±.012 .0902 ±.0045 .0942 ±.0052 .112 ±.003 3 .0828 ±.0042 .0832 ±.0042 .0890 ±.0092 .0820 ±.0037 .407 ±.035 .0827 ±.0042 .0822 ±.0019 .0814 ±.0029 .0820 ±.0053 .0982 ±.0041 .0907 ±.0097 .400 ±.056 .0827 ±.0024 .0819 ±.0044 .0838 ±.0032 4 .0835 ±.0043 .0859 ±.0041 .0888 ±.0070 .0841 ±.0038 .176 ±.130 .0855 ±.0038 .0845 ±.0030 .0839 ±.0047 .0839 ±.0038 .0934 ±.0019 .0838 ±.0038 .261 ±.146 .0858 ±.0048 .0837 ±.0041 .0843 ±.0032 5 .0810 ±.0026 .0814 ±.0038 .0817 ±.0025 .0810 ±.0030 .0809 ±.0050 .0819 ±.0033 .0808 ±.0027 .0801 ±.0028 .0809 ±.0023 .0820 ±.0031 .0802 ±.0026 .164 ±.147 .0805 ±.0029 .0809 ±.0036 .0812 ±.0030 predict_students_dropout and_academic_success 1 .717 ±.068 .896 ±.050 .812 ±.057 .765 ±.030 1.37 ±.08 .931 ±.161 .865 ±.097 .811 ±.064 .768 ±.033 1.02 ±.14 .728 ±.034 .861 ±.112 1.01 ±.06 .844 ±.053 .827 ±.108 2 .600 ±.008 .654 ±.029 .651 ±.020 .665 ±.014 1.17 ±.11 .646 ±.035 .618 ±.007 .635 ±.032 .630 ±.027 .730 ±.033 .648 ±.011 .744 ±.019 .656 ±.039 .606 ±.018 .733 ±.117 3 .593 ±.009 .604 ±.029 .587 ±.019 .621 ±.009 .703 ±.034 .603 ±.014 .596 ±.012 .622 ±.016 .583 ±.016 .628 ±.011 .595 ±.017 .691 ±.047 .624 ±.034 .594 ±.009 .621 ±.021 4 .589 ±.011 .581 ±.008 .587 ±.008 .622 ±.008 .621 ±.021 .590 ±.019 .586 ±.018 .616 ±.005 .588 ±.009 .605 ±.009 .586 ±.001 .625 ±.018 .585 ±.008 .581 ±.006 .593 ±.014 5 .583 ±.004 .580 ±.005 .581 ±.011 .602 ±.020 .582 ±.008 .585 ±.006 .585 ±.015 .619 ±.017 .584 ±.004 .595 ±.009 .581 ±.012 .591 ±.023 .586 ±.007 .581 ±.006 .583 ±.008 ancestry study 1 2.00 ±.22 2.29 ±.21 2.07 ±.33 2.27 ±.13 2.30 ±.15 2.20 ±.34 2.87 ±.15 3.28 ±.14 3.11 ±.91 3.23 ±.28 2.27 ±.43 3.18 ±.65 3.12 ±.93 3.40 ±.34 2.96 ±.85 2 3.33 ±.21 3.20 ±.33 2.31 ±.27 2.41 ±.08 2.29 ±.12 2.87 ±.79 3.40 ±.27 3.30 ±.38 3.33 ±.25 3.20 ±.20 2.86 ±.12 4.06 ±.72 3.51 ±.20 3.47 ±.41 3.70 ±.33 3 1.86 ±.14 2.13 ±.34 2.66 ±.62 2.87 ±.59 2.40 ±.48 2.55 ±.30 2.22 ±.46 1.91 ±.00 1.78 ±.10 1.88 ±.04 2.07 ±.15 2.93 ±.24 2.80 ±.17 1.90 ±.10 2.67 ±.31 4 1.67 ±.03 1.70 ±.07 1.71 ±.05 1.75 ±.02 3.38 ±.18 1.72 ±.04 1.74 ±.04 1.73 ±.04 1.63 ±.02 1.76 ±.02 1.71 ±.07 2.00 ±.20 1.99 ±.09 1.63 ±.01 1.87 ±.09 5 1.66 ±.02 1.70 ±.07 1.62 ±.02 1.76 ±.01 1.71 ±.08 1.70 ±.08 1.73 ±.08 1.77 ±.02 1.64 ±.02 1.76 ±.01 1.69 ±.05 1.91 ±.08 1.76 ±.10 1.68 ±.02 1.78 ±.03 otto_group_product classification_challenge 1 .837 ±.040 1.03 ±.06 1.17 ±.02 1.43 ±.01 1.19 ±.06 1.14 ±.02 1.21 ±.08 .874 ±.020 1.19 ±.01 1.02 ±.01 1.04 ±.05 1.48 ±.12 1.47 ±.14 .832 ±.003 1.45 ±.18 2 .624 ±.007 .679 ±.023 .753 ±.024 1.11 ±.01 .726 ±.010 .733 ±.045 .907 ±.045 .647 ±.006 .725 ±.008 .715 ±.006 .716 ±.013 .997 ±.008 .976 ±.029 .632 ±.008 .996 ±.148 3 .591 ±.006 .597 ±.004 .635 ±.003 .705 ±.005 .611 ±.007 .601 ±.005 .818 ±.087 .600 ±.009 .650 ±.004 .642 ±.004 .652 ±.009 .764 ±.043 .742 ±.056 .598 ±.008 .773 ±.049 4 .586 ±.007 .581 ±.006 .602 ±.002 .636 ±.005 .592 ±.002 .579 ±.006 .678 ±.009 .591 ±.006 .600 ±.003 .599 ±.005 .619 ±.004 .682 ±.013 .696 ±.053 .585 ±.005 .673 ±.048 5 .583 ±.006 .582 ±.007 .588 ±.006 .590 ±.008 .588 ±.007 .583 ±.007 .614 ±.010 .584 ±.007 .589 ±.008 .588 ±.005 .595 ±.009 .624 ±.024 .649 ±.005 .583 ±.006 .632 ±.013 lung_cancer 1 .360 ±.191 .464 ±.042 .559 ±.213 .294 ±.056 1.18 ±.17 .541 ±.220 .837 ±.188 .358 ±.160 .434 ±.116 .346 ±.146 .386 ±.068 .811 ±.209 .711 ±.044 .462 ±.310 .619 ±.146 2 .360 ±.095 .257 ±.019 .373 ±.038 .288 ±.021 .995 ±.060 .261 ±.071 .462 ±.087 .279 ±.023 .283 ±.044 .258 ±.013 .279 ±.038 .565 ±.142 .498 ±.023 .271 ±.159 .474 ±.070 3 .365 ±.058 .194 ±.005 .254 ±.021 .240 ±.009 .889 ±.099 .224 ±.008 .391 ±.026 .255 ±.022 .233 ±.021 .229 ±.017 .227 ±.006 .346 ±.021 .365 ±.050 .206 ±.013 .387 ±.046 4 .322 ±.033 .203 ±.008 .220 ±.013 .228 ±.026 .801 ±.056 .233 ±.013 .342 ±.020 .230 ±.027 .221 ±.017 .225 ±.019 .225 ±.010 .338 ±.017 .336 ±.038 .227 ±.014 .334 ±.030 5 .308 ±.030 .244 ±.022 .243 ±.023 .241 ±.015 .409 ±.088 .269 ±.009 .299 ±.007 .246 ±.020 .243 ±.018 .250 ±.011 .244 ±.016 .305 ±.012 .309 ±.016 .254 ±.015 .307 ±.011 soybean large 1 .867 ±.491 2.27 ±.29 1.22 ±.28 1.38 ±.38 1.43 ±.64 2.12 ±.56 1.82 ±.67 .633 ±.106 .703 ±.136 .731 ±.237 2.41 ±.15 2.16 ±.96 2.09 ±.98 .635 ±.201 1.01 ±.41 2 .561 ±.205 1.13 ±.15 1.01 ±.44 .829 ±.305 .937 ±.913 1.13 ±.12 .874 ±.199 .507 ±.077 .699 ±.047 .531 ±.031 1.20 ±.29 1.19 ±.47 .819 ±.376 .418 ±.056 .511 ±.108 3 .495 ±.126 .666 ±.063 .662 ±.289 .566 ±.355 .553 ±.174 .816 ±.230 .918 ±.248 .521 ±.086 .392 ±.133 .446 ±.019 .505 ±.025 .401 ±.025 .645 ±.215 .400 ±.023 .469 ±.165 4 .395 ±.021 .503 ±.130 .484 ±.176 .530 ±.399 .461 ±.235 .528 ±.045 .566 ±.087 .441 ±.123 .333 ±.042 .416 ±.028 .425 ±.038 .437 ±.013 .520 ±.132 .378 ±.023 .490 ±.102 5 .378 ±.038 .386 ±.027 .399 ±.100 .346 ±.044 .365 ±.123 .456 ±.039 .424 ±.023 .392 ±.067 .335 ±.035 .358 ±.026 .360 ±.038 .390 ±.026 .386 ±.043 .363 ±.036 .389 ±.046 mic 1 1.17 ±.04 1.36 ±.07 .896 ±.079 .690 ±.078 .710 ±.013 1.01 ±.42 1.72 ±.02 .890 ±.237 .716 ±.099 1.29 ±.15 .738 ±.113 .798 ±.074 .944 ±.165 .885 ±.149 .688 ±.168 2 .937 ±.106 1.17 ±.05 1.04 ±.07 .720 ±.048 .711 ±.012 1.18 ±.08 1.60 ±.11 .912 ±.136 .811 ±.048 1.08 ±.03 1.13 ±.16 1.15 ±.49 .942 ±.140 .925 ±.190 .715 ±.193 3 .646 ±.076 .739 ±.040 1.07 ±.08 .779 ±.072 .795 ±.108 .715 ±.091 1.05 ±.15 .824 ±.145 .957 ±.057 .625 ±.017 1.07 ±.12 1.03 ±.25 1.27 ±.10 .693 ±.095 1.28 ±.16 4 .554 ±.047 .597 ±.042 .796 ±.051 .870 ±.035 .973 ±.100 .581 ±.009 .709 ±.023 .623 ±.118 .583 ±.078 .530 ±.035 .768 ±.075 .834 ±.026 .985 ±.152 .568 ±.013 .827 ±.084 5 .506 ±.028 .572 ±.045 .522 ±.031 .651 ±.118 1.29 ±.05 .581 ±.005 .570 ±.069 .510 ±.032 .558 ±.097 .531 ±.022 .569 ±.085 .674 ±.068 .680 ±.111 .514 ±.028 .569 ±.103 ljubljana primary_tumor 1 3.28 ±.75 2.70 ±.42 2.10 ±.17 2.37 ±.08 2.16 ±.09 2.51 ±.29 2.35 ±.55 3.65 ±.54 2.84 ±.58 3.41 ±.24 2.31 ±.47 2.68 ±.38 2.31 ±.05 3.35 ±.14 2.33 ±.04 2 3.16 ±.54 3.19 ±.90 3.17 ±.19 3.51 ±.74 2.39 ±.40 2.73 ±.33 2.89 ±.33 3.32 ±.46 2.75 ±.68 3.44 ±.75 3.09 ±.04 2.92 ±.21 2.51 ±.66 3.10 ±.23 2.89 ±.63 3 2.51 ±.59 3.20 ±.59 2.83 ±.50 3.04 ±.62 3.63 ±.38 2.61 ±.16 2.95 ±.38 2.69 ±.65 2.41 ±.67 2.97 ±.67 2.84 ±.55 2.78 ±.94 2.64 ±.50 2.22 ±.25 2.73 ±.50 4 1.63 ±.17 2.33 ±.69 1.78 ±.22 2.14 ±.44 2.87 ±.63 2.21 ±.30 2.74 ±.42 2.14 ±.25 1.69 ±.29 2.22 ±.34 1.95 ±.42 2.42 ±.18 2.40 ±.40 1.64 ±.13 2.64 ±.44 5 1.57 ±.14 1.86 ±.41 1.63 ±.16 1.90 ±.46 1.94 ±.36 1.71 ±.22 1.91 ±.28 1.79 ±.35 1.57 ±.23 1.83 ±.43 1.71 ±.32 1.75 ±.29 2.04 ±.21 1.58 ±.22 2.13 ±.51 anneal 1 .131 ±.066 .165 ±.102 .254 ±.052 .107 ±.062 .521 ±.016 .150 ±.076 .174 ±.085 .0736 ±.0490 .117 ±.007 .0975 ±.0424 .146 ±.040 .612 ±.149 .121 ±.028 .0903 ±.0446 .0966 ±.0575 2 .0613 ±.0527 .0373 ±.0146 .121 ±.095 .0568 ±.0068 .445 ±.014 .0315 ±.0106 .0781 ±.0868 .0714 ±.0302 .0655 ±.0342 .0524 ±.0058 .0998 ±.0521 .314 ±.131 .0629 ±.0380 .0340 ±.0112 .0792 ±.0586 3 .0631 ±.0511 .0368 ±.0062 .0768 ±.0514 .0569 ±.0075 .329 ±.030 .0397 ±.0078 .0763 ±.0824 .0650 ±.0239 .0544 ±.0277 .0558 ±.0077 .0465 ±.0281 .167 ±.061 .0344 ±.0133 .0390 ±.0081 .0784 ±.0580 4 .0451 ±.0074 .0441 ±.0099 .0727 ±.0428 .0552 ±.0093 .200 ±.046 .0395 ±.0073 .0530 ±.0378 .0619 ±.0309 .0613 ±.0317 .0572 ±.0114 .0344 ±.0050 .162 ±.088 .0337 ±.0119 .0419 ±.0065 .0514 ±.0377 5 .0436 ±.0036 .0437 ±.0076 .0393 ±.0019 .0499 ±.0034 .0311 ±.0080 .0407 ±.0047 .0390 ±.0191 .0636 ±.0315 .0616 ±.0297 .0497 ±.0023 .0415 ±.0105 .0412 ±.0080 .0346 ±.0108 .0420 ±.0065 .0516 ±.0389 audiology diagnosis 1 .612 ±.331 .938 ±.476 .887 ±.222 .605 ±.480 .676 ±.296 .951 ±.476 1.09 ±.47 .562 ±.371 .526 ±.358 .836 ±.347 .689 ±.415 .832 ±.594 .656 ±.391 .532 ±.349 .760 ±.261 2 .532 ±.355 .842 ±.544 .533 ±.215 .527 ±.365 .605 ±.421 .821 ±.299 .890 ±.337 .527 ±.386 .497 ±.298 .663 ±.137 .606 ±.345 .654 ±.362 .638 ±.254 .496 ±.318 .648 ±.387 3 .514 ±.365 .572 ±.269 .414 ±.168 .447 ±.234 .478 ±.182 .684 ±.251 .673 ±.252 .476 ±.279 .489 ±.298 .492 ±.257 .458 ±.163 .463 ±.214 .630 ±.224 .420 ±.237 .548 ±.293 4 .423 ±.227 .401 ±.143 .421 ±.181 .432 ±.222 .498 ±.316 .363 ±.117 .632 ±.253 .423 ±.228 .432 ±.227 .452 ±.171 .418 ±.252 .401 ±.230 .539 ±.198 .466 ±.329 .566 ±.302 5 .413 ±.231 .415 ±.231 .428 ±.187 .422 ±.219 .391 ±.132 .407 ±.220 .653 ±.227 .373 ±.133 .424 ±.220 .476 ±.288 .461 ±.328 .383 ±.114 .432 ±.128 .473 ±.311 .457 ±.308 RMSE (↓) qsar_tid_11 1 1.34 ±.02 1.42 ±.03 1.39 ±.03 1.53 ±.02 1.40 ±.03 1.44 ±.03 1.45 ±.05 1.53 ±.01 1.36 ±.05 1.53 ±.02 1.49 ±.01 1.53 ±.00 1.53 ±.01 1.33 ±.02 1.37 ±.03 2 1.23 ±.01 1.27 ±.05 1.31 ±.03 1.50 ±.01 1.32 ±.00 1.26 ±.05 1.34 ±.06 1.47 ±.05 1.25 ±.02 1.50 ±.01 1.34 ±.06 1.50 ±.02 1.50 ±.04 1.20 ±.01 1.32 ±.03 3 1.01 ±.03 1.05 ±.01 1.04 ±.02 1.38 ±.04 1.17 ±.01 1.04 ±.02 1.18 ±.04 1.33 ±.04 1.01 ±.02 1.38 ±.04 1.05 ±.03 1.30 ±.02 1.30 ±.03 .979 ±.026 1.27 ±.06 4 .843 ±.010 .852 ±.028 .864 ±.011 1.04 ±.06 .950 ±.013 .853 ±.020 .980 ±.032 1.04 ±.02 .863 ±.015 1.04 ±.06 .857 ±.019 .980 ±.057 1.02 ±.09 .827 ±.015 .958 ±.026 5 .806 ±.017 .795 ±.017 .810 ±.008 .835 ±.011 .817 ±.004 .796 ±.018 .848 ±.026 .834 ±.046 .805 ±.018 .835 ±.011 .793 ±.019 .830 ±.024 .825 ±.011 .788 ±.009 .833 ±.025 61 (continued from previous page) DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCO MarkovBlanket MImRMR(R)ReliefFRandomSBERFImportanceSFS forest fires 1 1.55 ±.06 1.57 ±.11 1.62 ±.10 1.58 ±.11 1.47 ±.00 1.57 ±.11 1.57 ±.09 1.58 ±.09 1.54 ±.08 1.50 ±.13 1.58 ±.09 1.48 ±.06 1.46 ±.08 1.56 ±.07 1.42 ±.07 2 1.48 ±.05 1.51 ±.02 1.53 ±.12 1.51 ±.03 1.49 ±.05 1.51 ±.02 1.50 ±.06 1.47 ±.02 1.51 ±.08 1.55 ±.06 1.49 ±.03 1.50 ±.01 1.47 ±.02 1.49 ±.08 1.52 ±.07 3 1.47 ±.05 1.49 ±.04 1.48 ±.05 1.49 ±.05 1.48 ±.07 1.49 ±.04 1.48 ±.02 1.46 ±.01 1.48 ±.06 1.47 ±.06 1.48 ±.06 1.50 ±.03 1.47 ±.03 1.46 ±.03 1.47 ±.05 4 1.45 ±.05 1.47 ±.04 1.48 ±.04 1.47 ±.05 1.46 ±.04 1.47 ±.04 1.47 ±.02 1.46 ±.04 1.47 ±.07 1.46 ±.06 1.47 ±.07 1.46 ±.04 1.47 ±.03 1.46 ±.04 1.45 ±.06 5 1.46 ±.05 1.46 ±.04 1.47 ±.06 1.46 ±.05 1.46 ±.04 1.46 ±.04 1.47 ±.04 1.46 ±.04 1.47 ±.04 1.45 ±.05 1.47 ±.05 1.46 ±.04 1.47 ±.04 1.46 ±.04 1.47 ±.04 allstate claims_severity 1 .720 ±.002 .680 ±.002 .675 ±.002 .705 ±.010 .811 ±.002 .680 ±.002 .670 ±.001 .695 ±.001 .670 ±.002 .719 ±.011 .695 ±.003 .783 ±.024 .786 ±.024 .720 ±.002 .661 ±.001 2 .657 ±.003 .690 ±.001 .659 ±.001 .656 ±.003 .809 ±.002 .690 ±.001 .669 ±.016 .665 ±.001 .651 ±.001 .694 ±.014 .652 ±.011 .796 ±.010 .799 ±.018 .660 ±.005 .654 ±.013 3 .605 ±.001 .641 ±.006 .646 ±.001 .631 ±.011 .807 ±.002 .641 ±.006 .633 ±.007 .634 ±.002 .634 ±.001 .638 ±.002 .642 ±.015 .763 ±.020 .767 ±.024 .604 ±.001 .645 ±.015 4 .580 ±.002 .582 ±.005 .629 ±.005 .587 ±.003 .780 ±.010 .582 ±.005 .586 ±.006 .634 ±.003 .590 ±.003 .623 ±.004 .616 ±.006 .702 ±.034 .703 ±.027 .579 ±.001 .648 ±.008 5 .566 ±.001 .566 ±.003 .578 ±.002 .584 ±.002 .719 ±.011 .566 ±.003 .566 ±.005 .576 ±.001 .568 ±.002 .603 ±.010 .594 ±.014 .623 ±.022 .634 ±.024 .566 ±.001 .601 ±.005 miami housing 1 .259 ±.020 .276 ±.003 .346 ±.004 .283 ±.007 .330 ±.008 .276 ±.003 .352 ±.051 .205 ±.005 .214 ±.030 .324 ±.001 .381 ±.002 .454 ±.089 .205 ±.019 .268 ±.028 .272 ±.005 2 .188 ±.006 .233 ±.005 .243 ±.005 .186 ±.005 .230 ±.003 .233 ±.005 .229 ±.033 .193 ±.004 .195 ±.002 .192 ±.008 .251 ±.026 .300 ±.089 .173 ±.010 .191 ±.003 .184 ±.007 3 .165 ±.005 .185 ±.010 .181 ±.004 .179 ±.004 .227 ±.001 .190 ±.007 .172 ±.010 .186 ±.007 .180 ±.005 .180 ±.005 .196 ±.023 .219 ±.017 .163 ±.005 .165 ±.006 .165 ±.007 4 .163 ±.004 .172 ±.003 .161 ±.005 .173 ±.003 .225 ±.001 .171 ±.005 .162 ±.006 .173 ±.007 .173 ±.003 .170 ±.010 .163 ±.007 .192 ±.022 .160 ±.005 .161 ±.005 .162 ±.006 5 .160 ±.005 .161 ±.006 .161 ±.005 .160 ±.005 .206 ±.005 .161 ±.006 .160 ±.004 .160 ±.005 .160 ±.005 .163 ±.007 .160 ±.005 .174 ±.010 .160 ±.004 .160 ±.005 .160 ±.004 wine quality 1 .784 ±.016 .795 ±.012 .791 ±.008 .791 ±.008 .852 ±.010 .790 ±.007 .886 ±.009 .784 ±.016 .791 ±.008 .780 ±.006 .805 ±.018 .834 ±.037 .804 ±.008 .784 ±.016 .780 ±.006 2 .662 ±.008 .678 ±.006 .683 ±.007 .692 ±.009 .722 ±.011 .678 ±.006 .726 ±.012 .683 ±.013 .697 ±.016 .724 ±.015 .679 ±.015 .725 ±.040 .671 ±.013 .664 ±.006 .691 ±.005 3 .637 ±.009 .664 ±.006 .665 ±.010 .651 ±.002 .657 ±.006 .664 ±.005 .648 ±.017 .649 ±.012 .654 ±.016 .658 ±.005 .640 ±.010 .669 ±.025 .643 ±.012 .636 ±.009 .647 ±.008 4 .624 ±.011 .642 ±.005 .640 ±.007 .630 ±.010 .642 ±.007 .641 ±.006 .630 ±.009 .629 ±.012 .629 ±.014 .636 ±.009 .626 ±.011 .648 ±.013 .628 ±.010 .624 ±.011 .634 ±.010 5 .620 ±.011 .626 ±.008 .630 ±.007 .621 ±.009 .627 ±.006 .626 ±.007 .623 ±.007 .621 ±.010 .622 ±.014 .626 ±.011 .618 ±.011 .631 ±.013 .621 ±.009 .618 ±.011 .626 ±.010 body_density prediction 1 .0117 ±.0006 .0114 ±.0011 .0112 ±.0006 .0113 ±.0004 .0128 ±.0032 .0114 ±.0011 .0128 ±.0024 .0112 ±.0004 .0114 ±.0003 .0117 ±.0009 .0117 ±.0011 .0163 ±.0049 .0117 ±.0014 .0115 ±.0011 .0118 ±.0010 2 .0114 ±.0011 .0113 ±.0008 .0113 ±.0004 .0112 ±.0003 .0113 ±.0003 .0113 ±.0008 .0126 ±.0023 .0113 ±.0004 .0113 ±.0005 .0114 ±.0010 .0113 ±.0007 .0146 ±.0026 .0113 ±.0014 .0114 ±.0010 .0117 ±.0011 3 .0113 ±.0010 .0114 ±.0007 .0114 ±.0005 .0113 ±.0004 .0116 ±.0008 .0114 ±.0007 .0114 ±.0012 .0116 ±.0007 .0114 ±.0005 .0115 ±.0008 .0113 ±.0008 .0123 ±.0022 .0113 ±.0013 .0114 ±.0010 .0114 ±.0010 4 .0111 ±.0011 .0113 ±.0008 .0114 ±.0006 .0115 ±.0007 .0114 ±.0008 .0113 ±.0008 .0114 ±.0010 .0115 ±.0006 .0115 ±.0006 .0115 ±.0008 .0113 ±.0008 .0121 ±.0007 .0113 ±.0010 .0114 ±.0010 .0113 ±.0010 5 .0112 ±.0010 .0113 ±.0009 .0114 ±.0008 .0115 ±.0009 .0115 ±.0008 .0113 ±.0009 .0113 ±.0009 .0115 ±.0008 .0115 ±.0008 .0114 ±.0009 .0113 ±.0009 .0131 ±.0007 .0113 ±.0010 .0114 ±.0009 .0113 ±.0010 brasilian houses ( × 10 3 ) 1 7.18 ±1.59 7.02 ±1.59 7.18 ±1.59 11.1 ±3.5 15.4 ±1.1 7.02 ±1.59 7.30 ±1.08 7.18 ±1.59 11.1 ±3.5 7.18 ±1.59 7.12 ±1.21 8.89 ±.85 8.04 ±.42 7.18 ±1.59 11.4 ±2.7 2 6.89 ±1.58 6.92 ±1.52 6.93 ±1.52 7.21 ±1.62 15.0 ±4.2 6.88 ±1.58 6.88 ±1.54 6.87 ±1.59 6.89 ±1.56 6.88 ±1.60 6.92 ±1.54 12.9 ±2.7 6.89 ±1.53 6.89 ±1.58 8.59 ±2.85 3 6.87 ±1.59 6.87 ±1.61 6.86 ±1.60 6.87 ±1.59 10.7 ±1.3 6.88 ±1.60 6.87 ±1.52 6.88 ±1.61 6.89 ±1.61 6.90 ±1.55 6.87 ±1.58 7.41 ±2.26 6.94 ±1.53 6.87 ±1.59 8.56 ±3.36 4 6.89 ±1.57 6.88 ±1.56 6.89 ±1.57 6.89 ±1.58 10.4 ±1.2 6.88 ±1.56 6.88 ±1.53 6.89 ±1.61 6.90 ±1.58 6.89 ±1.60 6.88 ±1.59 10.6 ±6.2 6.92 ±1.53 6.89 ±1.57 10.2 ±5.9 5 6.89 ±1.60 6.88 ±1.60 6.88 ±1.60 6.89 ±1.60 9.97 ±1.58 6.89 ±1.60 6.89 ±1.54 6.88 ±1.60 6.90 ±1.58 6.88 ±1.60 6.88 ±1.61 6.88 ±1.59 6.92 ±1.55 6.89 ±1.59 6.91 ±1.53 automobile 1 .644 ±.050 .653 ±.170 .586 ±.159 .646 ±.109 1.12 ±.08 .655 ±.168 .709 ±.046 .806 ±.131 .613 ±.155 .775 ±.108 .662 ±.185 .775 ±.086 .730 ±.136 .613 ±.088 .768 ±.135 2 .615 ±.087 .670 ±.146 .635 ±.172 .714 ±.045 .916 ±.052 .670 ±.147 .683 ±.025 .760 ±.030 .645 ±.194 .714 ±.073 .652 ±.187 .778 ±.028 .660 ±.031 .612 ±.043 .709 ±.151 3 .617 ±.058 .712 ±.167 .672 ±.145 .722 ±.066 .783 ±.052 .713 ±.165 .643 ±.051 .622 ±.039 .664 ±.119 .694 ±.070 .628 ±.142 .726 ±.079 .648 ±.045 .631 ±.047 .733 ±.103 4 .622 ±.047 .706 ±.172 .661 ±.118 .661 ±.108 .710 ±.031 .705 ±.174 .652 ±.057 .605 ±.029 .620 ±.050 .642 ±.039 .670 ±.111 .655 ±.040 .650 ±.020 .627 ±.035 .669 ±.096 5 .630 ±.039 .650 ±.093 .629 ±.047 .680 ±.138 .669 ±.057 .651 ±.092 .631 ±.064 .617 ±.048 .627 ±.039 .617 ±.056 .614 ±.061 .631 ±.035 .622 ±.066 .624 ±.039 .652 ±.039 pva_revenue prediction_kddcup98 1 .649 ±.008 .599 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .599 ±.002 .601 ±.002 .600 ±.001 .604 ±.003 .612 ±.008 .598 ±.002 .600 ±.001 .601 ±.005 .647 ±.006 .605 ±.008 2 .611 ±.001 .644 ±.027 .627 ±.004 .598 ±.002 .611 ±.015 .643 ±.029 .627 ±.029 .631 ±.013 .640 ±.001 .644 ±.017 .611 ±.008 .637 ±.011 .640 ±.016 .610 ±.001 .644 ±.006 3 .611 ±.003 .611 ±.002 .612 ±.002 .621 ±.010 .635 ±.024 .611 ±.002 .622 ±.009 .630 ±.010 .625 ±.002 .615 ±.004 .639 ±.004 .614 ±.001 .617 ±.005 .611 ±.002 .615 ±.003 4 .613 ±.003 .611 ±.002 .613 ±.002 .620 ±.002 .623 ±.006 .611 ±.002 .614 ±.002 .611 ±.002 .614 ±.003 .612 ±.002 .613 ±.004 .614 ±.005 .614 ±.003 .613 ±.002 .613 ±.004 5 .615 ±.003 .615 ±.002 .617 ±.003 .612 ±.003 .624 ±.002 .616 ±.002 .614 ±.002 .611 ±.003 .612 ±.002 .610 ±.002 .612 ±.003 .614 ±.002 .614 ±.002 .615 ±.003 .613 ±.002 superconductivity 1 9.97 ±.25 9.86 ±.01 10.4 ±.1 9.93 ±.10 10.4 ±.2 10.0 ±.0 10.1 ±.1 9.98 ±.10 10.9 ±.6 9.91 ±.02 10.1 ±.2 9.97 ±.08 10.0 ±.1 9.90 ±.18 9.93 ±.20 2 9.73 ±.13 9.69 ±.07 9.74 ±.02 9.76 ±.06 9.72 ±.10 9.79 ±.12 9.80 ±.10 9.77 ±.09 9.64 ±.02 9.72 ±.16 9.76 ±.13 9.83 ±.14 9.78 ±.01 9.67 ±.09 9.72 ±.02 3 9.64 ±.07 9.71 ±.02 9.62 ±.10 9.64 ±.08 9.71 ±.12 9.66 ±.13 9.60 ±.07 9.69 ±.05 9.65 ±.03 9.58 ±.10 9.65 ±.04 9.72 ±.12 9.72 ±.11 9.66 ±.08 9.70 ±.00 4 9.62 ±.05 9.67 ±.05 9.62 ±.05 9.60 ±.06 9.64 ±.06 9.67 ±.07 9.57 ±.11 9.60 ±.08 9.54 ±.06 9.61 ±.07 9.53 ±.08 9.67 ±.09 9.74 ±.15 9.61 ±.08 9.60 ±.07 5 9.59 ±.05 9.60 ±.05 9.61 ±.05 9.61 ±.02 9.56 ±.06 9.62 ±.07 9.52 ±.07 9.56 ±.11 9.55 ±.07 9.61 ±.04 9.49 ±.05 9.59 ±.05 9.59 ±.06 9.58 ±.06 9.52 ±.11 AUC (↑) in_vehicle_coupon recommendation 1 .738 ±.010 .718 ±.006 .697 ±.008 .715 ±.012 .585 ±.016 .719 ±.007 .733 ±.010 .718 ±.010 .717 ±.024 .711 ±.019 .739 ±.011 .604 ±.042 .719 ±.008 .741 ±.006 .736 ±.008 2 .770 ±.006 .691 ±.007 .712 ±.016 .713 ±.008 .618 ±.005 .703 ±.021 .757 ±.033 .718 ±.005 .712 ±.028 .715 ±.010 .769 ±.006 .631 ±.021 .768 ±.009 .767 ±.001 .734 ±.013 3 .775 ±.003 .700 ±.009 .672 ±.005 .776 ±.003 .623 ±.008 .706 ±.007 .789 ±.001 .778 ±.007 .756 ±.023 .701 ±.006 .777 ±.004 .698 ±.050 .796 ±.009 .774 ±.002 .761 ±.030 4 .806 ±.008 .746 ±.007 .776 ±.020 .796 ±.004 .688 ±.005 .748 ±.017 .796 ±.014 .794 ±.003 .795 ±.001 .754 ±.005 .790 ±.013 .775 ±.011 .811 ±.007 .812 ±.006 .778 ±.037 5 .810 ±.005 .794 ±.001 .799 ±.003 .804 ±.007 .777 ±.003 .795 ±.001 .808 ±.005 .800 ±.011 .803 ±.009 .791 ±.003 .804 ±.008 .793 ±.013 .807 ±.009 .809 ±.007 .801 ±.014 marketing campaign 1 .806 ±.040 .804 ±.046 .749 ±.022 .767 ±.023 .701 ±.015 .808 ±.043 .775 ±.040 .793 ±.016 .761 ±.038 .800 ±.035 .716 ±.026 .748 ±.037 .810 ±.024 .821 ±.027 .775 ±.049 2 .849 ±.025 .857 ±.031 .870 ±.020 .817 ±.022 .787 ±.027 .867 ±.035 .852 ±.025 .839 ±.012 .819 ±.028 .845 ±.030 .805 ±.039 .782 ±.013 .856 ±.032 .843 ±.033 .835 ±.015 3 .862 ±.019 .880 ±.031 .869 ±.021 .856 ±.017 .857 ±.015 .887 ±.026 .872 ±.038 .854 ±.006 .854 ±.019 .870 ±.028 .860 ±.029 .820 ±.027 .873 ±.025 .857 ±.026 .878 ±.031 4 .876 ±.024 .886 ±.028 .880 ±.019 .870 ±.017 .843 ±.014 .888 ±.027 .881 ±.033 .858 ±.014 .872 ±.017 .872 ±.033 .874 ±.029 .864 ±.037 .879 ±.023 .868 ±.027 .886 ±.030 5 .878 ±.029 .887 ±.022 .878 ±.023 .869 ±.023 .866 ±.035 .886 ±.026 .882 ±.034 .880 ±.019 .882 ±.024 .878 ±.030 .881 ±.025 .871 ±.033 .880 ±.027 .880 ±.025 .880 ±.028 polish_companies bankruptcy 1 .940 ±.016 .815 ±.003 .847 ±.016 .877 ±.021 .828 ±.015 .809 ±.012 .917 ±.014 .921 ±.019 .904 ±.029 .899 ±.014 .842 ±.005 .830 ±.016 .877 ±.048 .934 ±.011 .916 ±.022 2 .935 ±.015 .840 ±.015 .860 ±.011 .911 ±.028 .846 ±.016 .837 ±.017 .915 ±.024 .929 ±.011 .919 ±.016 .920 ±.016 .912 ±.027 .880 ±.049 .922 ±.013 .935 ±.015 .918 ±.020 3 .934 ±.015 .872 ±.034 .856 ±.011 .926 ±.018 .850 ±.019 .876 ±.030 .915 ±.016 .931 ±.013 .925 ±.019 .928 ±.015 .907 ±.022 .860 ±.020 .930 ±.016 .933 ±.012 .918 ±.019 4 .933 ±.010 .877 ±.028 .884 ±.024 .925 ±.013 .855 ±.004 .879 ±.025 .914 ±.019 .929 ±.012 .922 ±.012 .931 ±.013 .915 ±.018 .909 ±.013 .930 ±.013 .932 ±.013 .923 ±.012 5 .929 ±.010 .911 ±.021 .909 ±.018 .923 ±.010 .862 ±.007 .909 ±.024 .918 ±.016 .929 ±.010 .925 ±.009 .931 ±.009 .914 ±.019 .911 ±.017 .926 ±.009 .929 ±.015 .927 ±.016 colon_tumor 1 .710 ±.030 .865 ±.032 .872 ±.121 .835 ±.085 .577 ±.230 .854 ±.050 .643 ±.219 .854 ±.092 .839 ±.062 .845 ±.053 .915 ±.059 .635 ±.120 .724 ±.173 .793 ±.174 .689 ±.093 2 .798 ±.022 .885 ±.069 .879 ±.070 .877 ±.092 .811 ±.183 .921 ±.039 .892 ±.073 .845 ±.156 .853 ±.067 .868 ±.100 .903 ±.062 .793 ±.138 .811 ±.175 .879 ±.085 .789 ±.110 3 .841 ±.076 .874 ±.070 .895 ±.059 .868 ±.082 .871 ±.083 .892 ±.088 .859 ±.166 .865 ±.090 .878 ±.090 .863 ±.099 .901 ±.065 .880 ±.107 .828 ±.143 .863 ±.095 .900 ±.113 4 .878 ±.065 .884 ±.077 .891 ±.079 .888 ±.066 .905 ±.068 .916 ±.056 .881 ±.091 .874 ±.072 .885 ±.071 .892 ±.065 .895 ±.061 .901 ±.056 .892 ±.079 .885 ±.069 .883 ±.071 5 .897 ±.059 .868 ±.098 .895 ±.077 .895 ±.061 .908 ±.080 .903 ±.045 .914 ±.077 .880 ±.059 .909 ±.081 .901 ±.065 .893 ±.062 .889 ±.070 .895 ±.069 .888 ±.071 .909 ±.056 62 (continued from previous page) DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCO MarkovBlanket MImRMR(R)ReliefFRandomSBERFImportanceSFS mechanisms of_action 1 .940 ±.004 .937 ±.005 .934 ±.006 .936 ±.009 .932 ±.010 .937 ±.012 .909 ±.039 .930 ±.007 .938 ±.010 .933 ±.008 .832 ±.103 .914 ±.014 .933 ±.022 .934 ±.010 .932 ±.012 2 .937 ±.008 .938 ±.013 .937 ±.008 .941 ±.003 .941 ±.008 .939 ±.017 .933 ±.009 .932 ±.016 .930 ±.011 .939 ±.008 .934 ±.008 .934 ±.009 .931 ±.006 .935 ±.007 .935 ±.008 3 .937 ±.008 .943 ±.012 .940 ±.014 .937 ±.012 .939 ±.012 .936 ±.014 .936 ±.012 .936 ±.019 .941 ±.016 .936 ±.009 .937 ±.008 .933 ±.013 .935 ±.015 .939 ±.008 .936 ±.016 4 .936 ±.010 .934 ±.009 .933 ±.004 .943 ±.008 .937 ±.010 .934 ±.007 .937 ±.010 .936 ±.012 .941 ±.016 .933 ±.014 .938 ±.010 .931 ±.015 .935 ±.010 .943 ±.014 .938 ±.007 5 .937 ±.011 .934 ±.010 .938 ±.011 .937 ±.012 .936 ±.008 .930 ±.011 .937 ±.016 .936 ±.014 .937 ±.009 .936 ±.011 .933 ±.009 .939 ±.008 .937 ±.012 .937 ±.013 .940 ±.013 santander_customer transaction_prediction 1 .577 ±.003 .577 ±.002 .582 ±.003 .582 ±.003 .501 ±.001 .577 ±.002 .545 ±.019 .581 ±.004 .502 ±.002 .582 ±.003 .504 ±.007 .500 ±.005 .509 ±.000 .582 ±.003 .532 ±.011 2 .674 ±.003 .677 ±.001 .680 ±.002 .679 ±.002 .501 ±.001 .677 ±.002 .601 ±.023 .677 ±.004 .508 ±.002 .679 ±.002 .498 ±.005 .549 ±.028 .549 ±.034 .677 ±.002 .554 ±.022 3 .762 ±.005 .766 ±.004 .766 ±.003 .762 ±.004 .512 ±.001 .765 ±.003 .622 ±.017 .763 ±.001 .512 ±.004 .767 ±.002 .501 ±.002 .580 ±.035 .573 ±.014 .766 ±.002 .605 ±.018 4 .825 ±.004 .830 ±.002 .830 ±.003 .827 ±.004 .516 ±.002 .830 ±.002 .644 ±.011 .824 ±.003 .537 ±.003 .826 ±.002 .507 ±.005 .624 ±.010 .610 ±.018 .830 ±.004 .656 ±.008 5 .839 ±.004 .842 ±.003 .844 ±.002 .842 ±.001 .522 ±.002 .842 ±.002 .744 ±.015 .841 ±.003 .668 ±.003 .844 ±.002 .542 ±.021 .736 ±.020 .728 ±.010 .839 ±.002 .746 ±.016 nci_prostate cancer 1 .922 ±.035 .932 ±.010 .921 ±.011 .921 ±.018 .533 ±.029 .897 ±.068 .729 ±.080 .913 ±.055 .910 ±.019 .953 ±.011 .867 ±.053 .790 ±.031 .713 ±.071 .936 ±.018 .767 ±.055 2 .952 ±.005 .960 ±.013 .951 ±.016 .962 ±.009 .694 ±.085 .980 ±.006 .891 ±.027 .971 ±.009 .961 ±.003 .965 ±.002 .959 ±.011 .871 ±.031 .887 ±.036 .956 ±.016 .868 ±.046 3 .972 ±.015 .978 ±.005 .971 ±.003 .975 ±.009 .846 ±.043 .985 ±.004 .943 ±.009 .973 ±.018 .980 ±.004 .979 ±.005 .978 ±.011 .917 ±.040 .921 ±.018 .982 ±.008 .938 ±.021 4 .965 ±.013 .986 ±.005 .978 ±.005 .978 ±.006 .913 ±.021 .985 ±.005 .952 ±.022 .975 ±.015 .984 ±.004 .982 ±.006 .980 ±.006 .938 ±.009 .939 ±.015 .985 ±.007 .943 ±.013 5 .962 ±.019 .986 ±.006 .985 ±.008 .984 ±.006 .943 ±.018 .986 ±.004 .968 ±.014 .978 ±.013 .981 ±.013 .982 ±.007 .989 ±.003 .958 ±.014 .961 ±.011 .987 ±.006 .958 ±.019 bank_customer churn 1 .663 ±.005 .762 ±.013 .762 ±.013 .807 ±.005 .597 ±.011 .762 ±.013 .807 ±.005 .807 ±.005 .807 ±.005 .807 ±.005 .748 ±.055 .504 ±.009 .807 ±.005 .664 ±.003 .807 ±.005 2 .724 ±.006 .720 ±.008 .720 ±.008 .796 ±.002 .766 ±.002 .720 ±.008 .794 ±.002 .795 ±.001 .791 ±.023 .793 ±.009 .769 ±.003 .613 ±.082 .786 ±.013 .723 ±.006 .804 ±.001 3 .809 ±.005 .747 ±.011 .780 ±.024 .814 ±.009 .783 ±.008 .747 ±.011 .819 ±.007 .813 ±.010 .809 ±.023 .784 ±.011 .805 ±.002 .664 ±.087 .813 ±.009 .809 ±.005 .798 ±.005 4 .832 ±.004 .817 ±.034 .836 ±.003 .829 ±.009 .759 ±.012 .817 ±.034 .836 ±.015 .830 ±.007 .833 ±.008 .800 ±.012 .825 ±.006 .761 ±.062 .841 ±.009 .827 ±.007 .830 ±.020 5 .850 ±.002 .829 ±.031 .851 ±.005 .841 ±.011 .784 ±.009 .829 ±.031 .847 ±.005 .843 ±.003 .850 ±.004 .828 ±.002 .838 ±.005 .819 ±.040 .849 ±.005 .850 ±.004 .848 ±.006 hepatitis_survival prediction 1 .768 ±.044 .781 ±.086 .769 ±.002 .825 ±.026 .773 ±.064 .813 ±.033 .806 ±.029 .822 ±.016 .749 ±.017 .834 ±.045 .777 ±.093 .803 ±.013 .773 ±.090 .799 ±.058 .758 ±.071 2 .805 ±.051 .836 ±.038 .845 ±.006 .825 ±.002 .782 ±.080 .832 ±.028 .829 ±.042 .848 ±.040 .799 ±.034 .823 ±.033 .808 ±.029 .787 ±.063 .847 ±.069 .813 ±.070 .754 ±.069 3 .833 ±.043 .843 ±.034 .846 ±.040 .827 ±.016 .808 ±.032 .831 ±.041 .829 ±.050 .845 ±.054 .853 ±.025 .823 ±.035 .839 ±.034 .845 ±.017 .858 ±.067 .827 ±.051 .777 ±.065 4 .853 ±.063 .836 ±.066 .863 ±.052 .855 ±.063 .864 ±.032 .851 ±.056 .860 ±.080 .875 ±.052 .866 ±.045 .859 ±.053 .861 ±.022 .832 ±.063 .880 ±.070 .868 ±.061 .829 ±.059 5 .855 ±.037 .844 ±.048 .854 ±.038 .867 ±.040 .850 ±.038 .848 ±.042 .868 ±.059 .856 ±.057 .864 ±.036 .850 ±.055 .870 ±.033 .867 ±.067 .880 ±.031 .857 ±.054 .850 ±.059 customer_satisfaction in_airline 1 .957 ±.001 .920 ±.001 .934 ±.002 .923 ±.000 .940 ±.001 .920 ±.001 .941 ±.002 .961 ±.000 .958 ±.000 .913 ±.000 .956 ±.001 .827 ±.079 .968 ±.002 .958 ±.000 .971 ±.001 2 .970 ±.001 .974 ±.000 .951 ±.002 .971 ±.001 .977 ±.000 .974 ±.000 .965 ±.001 .974 ±.000 .973 ±.000 .965 ±.000 .973 ±.006 .866 ±.015 .982 ±.001 .960 ±.002 .981 ±.001 3 .986 ±.001 .983 ±.001 .974 ±.009 .980 ±.001 .983 ±.000 .983 ±.001 .985 ±.002 .981 ±.001 .981 ±.001 .979 ±.000 .984 ±.000 .971 ±.012 .987 ±.000 .981 ±.000 .987 ±.000 4 .990 ±.000 .987 ±.000 .988 ±.000 .987 ±.000 .988 ±.001 .987 ±.000 .991 ±.000 .987 ±.000 .986 ±.001 .988 ±.000 .989 ±.000 .987 ±.003 .990 ±.000 .988 ±.001 .990 ±.000 5 .992 ±.000 .992 ±.000 .991 ±.000 .989 ±.000 .992 ±.000 .992 ±.000 .993 ±.000 .991 ±.000 .992 ±.001 .991 ±.000 .992 ±.001 .991 ±.001 .992 ±.000 .993 ±.000 .992 ±.000 heloc 1 .749 ±.005 .715 ±.005 .750 ±.004 .707 ±.006 .635 ±.006 .723 ±.010 .722 ±.051 .750 ±.011 .718 ±.008 .739 ±.010 .682 ±.013 .668 ±.048 .750 ±.005 .752 ±.008 .735 ±.016 2 .770 ±.009 .764 ±.008 .777 ±.011 .753 ±.002 .721 ±.008 .767 ±.001 .768 ±.018 .777 ±.004 .775 ±.015 .782 ±.008 .781 ±.006 .736 ±.017 .780 ±.012 .773 ±.008 .776 ±.005 3 .785 ±.008 .775 ±.004 .786 ±.006 .783 ±.004 .740 ±.006 .776 ±.004 .776 ±.018 .788 ±.005 .786 ±.006 .788 ±.006 .787 ±.007 .760 ±.012 .786 ±.008 .786 ±.007 .785 ±.009 4 .789 ±.008 .788 ±.006 .788 ±.006 .788 ±.007 .772 ±.006 .787 ±.006 .787 ±.009 .790 ±.006 .789 ±.007 .789 ±.005 .789 ±.008 .778 ±.007 .788 ±.008 .788 ±.008 .788 ±.009 5 .791 ±.007 .790 ±.007 .791 ±.007 .792 ±.007 .788 ±.009 .790 ±.007 .789 ±.007 .792 ±.009 .790 ±.006 .790 ±.008 .791 ±.009 .787 ±.004 .789 ±.009 .791 ±.007 .789 ±.009 hiva agnostic 1 .597 ±.033 .669 ±.046 .548 ±.040 .526 ±.052 .540 ±.008 .589 ±.045 .514 ±.066 .517 ±.054 .610 ±.071 .526 ±.052 .610 ±.064 .591 ±.027 .577 ±.037 .589 ±.068 .676 ±.049 2 .653 ±.018 .709 ±.047 .617 ±.094 .612 ±.007 .573 ±.019 .726 ±.015 .638 ±.063 .628 ±.041 .605 ±.093 .612 ±.007 .638 ±.068 .659 ±.054 .604 ±.054 .679 ±.040 .635 ±.063 3 .673 ±.028 .700 ±.038 .659 ±.068 .691 ±.036 .626 ±.058 .698 ±.027 .704 ±.015 .720 ±.043 .681 ±.053 .691 ±.036 .740 ±.042 .662 ±.016 .646 ±.008 .731 ±.043 .728 ±.028 4 .741 ±.027 .767 ±.019 .740 ±.037 .769 ±.025 .685 ±.021 .779 ±.020 .744 ±.010 .758 ±.018 .741 ±.036 .769 ±.025 .771 ±.012 .754 ±.037 .751 ±.038 .787 ±.005 .748 ±.026 5 .761 ±.012 .780 ±.020 .777 ±.016 .751 ±.014 .747 ±.044 .757 ±.010 .759 ±.031 .776 ±.019 .775 ±.014 .751 ±.014 .763 ±.014 .777 ±.015 .760 ±.009 .774 ±.016 .774 ±.011 bank marketing 1 .644 ±.017 .686 ±.006 .686 ±.006 .646 ±.005 .624 ±.007 .686 ±.006 .669 ±.033 .669 ±.006 .612 ±.017 .640 ±.005 .673 ±.011 .672 ±.022 .707 ±.006 .650 ±.008 .714 ±.009 2 .663 ±.009 .690 ±.005 .696 ±.008 .694 ±.008 .649 ±.007 .690 ±.005 .679 ±.026 .669 ±.010 .655 ±.014 .628 ±.007 .654 ±.009 .671 ±.031 .706 ±.030 .661 ±.013 .730 ±.010 3 .676 ±.008 .712 ±.007 .678 ±.036 .675 ±.010 .728 ±.008 .712 ±.007 .674 ±.014 .686 ±.006 .687 ±.010 .664 ±.006 .656 ±.008 .681 ±.019 .703 ±.023 .676 ±.008 .727 ±.009 4 .705 ±.010 .702 ±.004 .682 ±.007 .704 ±.007 .714 ±.009 .698 ±.011 .691 ±.012 .705 ±.008 .711 ±.005 .689 ±.007 .674 ±.010 .685 ±.020 .705 ±.018 .686 ±.012 .693 ±.012 5 .723 ±.003 .712 ±.010 .710 ±.006 .713 ±.007 .709 ±.006 .714 ±.010 .712 ±.025 .717 ±.013 .719 ±.010 .711 ±.006 .710 ±.004 .700 ±.014 .712 ±.020 .722 ±.005 .690 ±.005 leukemia allaml 1 .904 ±.059 .992 ±.014 .997 ±.005 .998 ±.004 .709 ±.218 .979 ±.030 .668 ±.154 .988 ±.015 .992 ±.007 .967 ±.038 .995 ±.004 .731 ±.244 .657 ±.236 .987 ±.011 .581 ±.043 2 .957 ±.033 .998 ±.004 .993 ±.013 .997 ±.005 .916 ±.012 .998 ±.004 .784 ±.118 .983 ±.030 .999 ±.002 .997 ±.005 .995 ±.009 .707 ±.136 .863 ±.077 .995 ±.006 .849 ±.152 3 .955 ±.059 .992 ±.007 .998 ±.004 .997 ±.005 .927 ±.041 .983 ±.024 .949 ±.045 .980 ±.034 .997 ±.005 1.00 ±.00 .994 ±.008 .971 ±.030 .957 ±.012 1.00 ±.00 .957 ±.020 4 .960 ±.056 1.00 ±.00 .998 ±.004 1.00 ±.00 .951 ±.053 .983 ±.030 .961 ±.047 .985 ±.026 .997 ±.005 .997 ±.005 .997 ±.005 .962 ±.038 .970 ±.039 .998 ±.004 .958 ±.051 5 .978 ±.032 1.00 ±.00 .998 ±.004 .994 ±.006 .986 ±.013 .979 ±.036 .993 ±.013 .994 ±.011 1.00 ±.00 1.00 ±.00 .998 ±.004 .985 ±.026 .992 ±.008 .993 ±.013 .988 ±.021 heart_disease cleveland 1 .830 ±.011 .834 ±.006 .794 ±.040 .834 ±.058 .750 ±.022 .826 ±.046 .815 ±.074 .775 ±.018 .826 ±.003 .794 ±.073 .840 ±.031 .825 ±.031 .810 ±.086 .749 ±.026 .798 ±.074 2 .854 ±.037 .838 ±.013 .845 ±.011 .884 ±.015 .790 ±.052 .828 ±.036 .810 ±.067 .828 ±.032 .862 ±.021 .828 ±.020 .851 ±.021 .833 ±.005 .815 ±.062 .868 ±.035 .826 ±.042 3 .878 ±.013 .881 ±.017 .874 ±.021 .899 ±.021 .850 ±.019 .871 ±.022 .855 ±.028 .874 ±.012 .881 ±.022 .849 ±.017 .880 ±.007 .818 ±.033 .858 ±.020 .895 ±.039 .858 ±.032 4 .884 ±.019 .894 ±.024 .895 ±.021 .904 ±.038 .868 ±.025 .889 ±.017 .860 ±.045 .901 ±.036 .889 ±.028 .892 ±.020 .885 ±.020 .905 ±.029 .866 ±.026 .892 ±.030 .883 ±.039 5 .900 ±.028 .904 ±.026 .906 ±.028 .895 ±.029 .892 ±.020 .894 ±.032 .889 ±.038 .907 ±.040 .902 ±.036 .900 ±.034 .901 ±.021 .889 ±.048 .890 ±.044 .906 ±.032 .893 ±.029 clock_protein period 1 .498 ±.163 .655 ±.123 .565 ±.043 .496 ±.155 .638 ±.069 .655 ±.123 .477 ±.099 .515 ±.034 .495 ±.080 .504 ±.066 .547 ±.092 .638 ±.134 .601 ±.054 .561 ±.128 .583 ±.028 2 .513 ±.041 .645 ±.083 .558 ±.160 .493 ±.194 .617 ±.050 .645 ±.083 .490 ±.103 .519 ±.015 .534 ±.061 .521 ±.148 .558 ±.149 .569 ±.058 .555 ±.106 .557 ±.138 .640 ±.073 3 .557 ±.059 .617 ±.051 .556 ±.166 .532 ±.138 .623 ±.052 .617 ±.051 .534 ±.105 .574 ±.124 .570 ±.104 .523 ±.160 .523 ±.149 .622 ±.094 .627 ±.069 .576 ±.135 .616 ±.099 4 .568 ±.113 .582 ±.116 .536 ±.161 .569 ±.127 .617 ±.067 .582 ±.116 .558 ±.068 .566 ±.130 .586 ±.119 .490 ±.146 .522 ±.189 .556 ±.110 .599 ±.133 .563 ±.123 .618 ±.089 5 .552 ±.129 .570 ±.118 .615 ±.131 .593 ±.113 .659 ±.045 .570 ±.118 .579 ±.089 .599 ±.096 .584 ±.114 .538 ±.134 .541 ±.112 .567 ±.120 .576 ±.120 .574 ±.099 .552 ±.109 churn 1 .802 ±.021 .839 ±.006 .827 ±.004 .770 ±.016 .576 ±.016 .734 ±.199 .691 ±.016 .792 ±.005 .828 ±.005 .821 ±.032 .772 ±.012 .657 ±.128 .879 ±.007 .828 ±.005 .879 ±.008 2 .892 ±.034 .898 ±.003 .898 ±.004 .832 ±.007 .680 ±.110 .836 ±.100 .783 ±.112 .847 ±.003 .869 ±.022 .876 ±.013 .812 ±.007 .687 ±.099 .909 ±.008 .896 ±.010 .905 ±.004 3 .919 ±.009 .908 ±.002 .902 ±.014 .867 ±.010 .810 ±.014 .912 ±.007 .824 ±.078 .856 ±.006 .893 ±.013 .913 ±.006 .902 ±.003 .747 ±.124 .916 ±.002 .908 ±.007 .909 ±.007 4 .916 ±.002 .916 ±.009 .919 ±.011 .868 ±.011 .809 ±.005 .920 ±.007 .868 ±.072 .856 ±.009 .907 ±.008 .914 ±.003 .907 ±.011 .838 ±.033 .917 ±.004 .916 ±.004 .914 ±.001 5 .915 ±.005 .916 ±.005 .917 ±.001 .864 ±.009 .910 ±.001 .915 ±.005 .913 ±.004 .854 ±.006 .908 ±.008 .914 ±.002 .906 ±.008 .875 ±.069 .917 ±.004 .915 ±.002 .916 ±.003 aps_failure 1 .944 ±.005 .935 ±.062 .956 ±.008 .903 ±.005 .925 ±.013 .940 ±.039 .903 ±.066 .971 ±.012 .974 ±.007 .898 ±.003 .830 ±.015 .927 ±.063 .865 ±.005 .972 ±.008 .933 ±.034 2 .979 ±.003 .974 ±.018 .969 ±.005 .933 ±.004 .948 ±.010 .967 ±.019 .968 ±.009 .981 ±.006 .979 ±.005 .962 ±.011 .957 ±.003 .970 ±.014 .957 ±.010 .981 ±.005 .974 ±.005 3 .986 ±.001 .987 ±.004 .980 ±.005 .949 ±.014 .977 ±.000 .987 ±.002 .985 ±.005 .987 ±.006 .981 ±.006 .978 ±.002 .982 ±.005 .983 ±.010 .985 ±.002 .987 ±.005 .984 ±.007 4 .989 ±.003 .989 ±.004 .986 ±.005 .989 ±.004 .985 ±.004 .990 ±.003 .988 ±.004 .989 ±.003 .986 ±.004 .985 ±.003 .985 ±.005 .988 ±.004 .988 ±.003 .989 ±.003 .986 ±.008 5 .990 ±.002 .990 ±.004 .990 ±.004 .991 ±.003 .989 ±.003 .990 ±.002 .989 ±.004 .990 ±.004 .989 ±.004 .989 ±.004 .987 ±.005 .989 ±.004 .990 ±.004 .990 ±.002 .989 ±.004 63 (continued from previous page) DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCO MarkovBlanket MImRMR(R)ReliefFRandomSBERFImportanceSFS labor negotiations 1 .902 ±.034 .835 ±.131 .890 ±.095 .862 ±.100 .936 ±.048 .776 ±.172 .832 ±.140 .949 ±.035 .908 ±.095 .880 ±.069 .944 ±.060 .754 ±.164 .813 ±.256 .908 ±.095 .840 ±.133 2 .929 ±.051 .960 ±.049 .911 ±.049 .918 ±.031 .967 ±.030 .943 ±.065 .887 ±.115 .943 ±.042 .955 ±.019 .938 ±.034 .947 ±.020 .767 ±.298 .855 ±.171 .955 ±.024 .861 ±.171 3 .943 ±.051 .975 ±.021 .947 ±.030 .947 ±.027 .967 ±.030 .958 ±.039 .942 ±.050 .963 ±.036 .963 ±.032 .922 ±.037 .965 ±.031 .936 ±.081 .959 ±.036 .980 ±.034 .941 ±.036 4 .976 ±.041 .980 ±.025 .976 ±.031 .972 ±.025 .972 ±.027 .980 ±.025 .968 ±.045 .972 ±.048 .980 ±.034 .962 ±.019 .963 ±.032 .975 ±.013 .976 ±.031 .978 ±.024 .959 ±.018 5 .980 ±.018 .984 ±.018 .976 ±.024 .978 ±.021 .967 ±.008 .976 ±.024 .976 ±.024 .988 ±.021 .978 ±.024 .976 ±.024 .973 ±.024 .971 ±.020 .976 ±.024 .980 ±.025 .971 ±.015 kdd_cup_09 appetency 1 .578 ±.085 .514 ±.018 .573 ±.009 .743 ±.018 .535 ±.024 .514 ±.018 .685 ±.091 .565 ±.004 .580 ±.016 .747 ±.017 .596 ±.011 .503 ±.012 .497 ±.004 .647 ±.017 .783 ±.012 2 .687 ±.059 .518 ±.017 .709 ±.022 .730 ±.022 .547 ±.034 .518 ±.017 .682 ±.009 .729 ±.008 .583 ±.007 .571 ±.014 .578 ±.004 .527 ±.025 .511 ±.017 .724 ±.012 .728 ±.047 3 .755 ±.063 .532 ±.016 .741 ±.025 .722 ±.057 .544 ±.037 .532 ±.016 .715 ±.047 .765 ±.003 .686 ±.077 .662 ±.013 .749 ±.015 .532 ±.016 .532 ±.023 .755 ±.019 .704 ±.035 4 .776 ±.022 .558 ±.036 .769 ±.023 .766 ±.024 .544 ±.017 .558 ±.036 .741 ±.043 .769 ±.011 .746 ±.002 .783 ±.019 .772 ±.019 .652 ±.061 .623 ±.054 .735 ±.035 .750 ±.005 5 .770 ±.023 .669 ±.096 .780 ±.023 .771 ±.015 .620 ±.024 .669 ±.096 .746 ±.042 .767 ±.018 .751 ±.012 .778 ±.021 .778 ±.013 .695 ±.042 .695 ±.049 .776 ±.016 .771 ±.017 qsar_oral toxicity 1 .720 ±.015 .692 ±.042 .692 ±.027 .569 ±.061 .564 ±.016 .695 ±.034 .558 ±.042 .553 ±.041 .686 ±.022 .569 ±.061 .566 ±.023 .579 ±.014 .573 ±.012 .693 ±.028 .733 ±.014 2 .767 ±.006 .743 ±.029 .710 ±.027 .616 ±.021 .577 ±.008 .778 ±.021 .629 ±.025 .636 ±.039 .724 ±.020 .616 ±.021 .686 ±.015 .637 ±.048 .643 ±.015 .733 ±.044 .734 ±.018 3 .831 ±.024 .820 ±.020 .789 ±.029 .730 ±.037 .715 ±.014 .826 ±.019 .746 ±.015 .756 ±.019 .810 ±.033 .730 ±.037 .803 ±.022 .760 ±.028 .754 ±.011 .816 ±.023 .791 ±.036 4 .876 ±.022 .865 ±.027 .869 ±.026 .837 ±.023 .840 ±.017 .864 ±.030 .851 ±.012 .857 ±.036 .863 ±.022 .837 ±.023 .884 ±.013 .842 ±.020 .853 ±.001 .880 ±.023 .859 ±.027 5 .895 ±.023 .890 ±.024 .890 ±.028 .894 ±.023 .889 ±.015 .890 ±.031 .890 ±.012 .894 ±.019 .895 ±.017 .894 ±.023 .897 ±.027 .887 ±.027 .885 ±.025 .896 ±.023 .896 ±.020 taiwanese_bankruptcy prediction 1 .922 ±.003 .921 ±.006 .915 ±.020 .927 ±.016 .926 ±.017 .916 ±.020 .919 ±.012 .916 ±.010 .925 ±.005 .924 ±.010 .900 ±.010 .923 ±.013 .919 ±.013 .931 ±.002 .930 ±.012 2 .930 ±.013 .933 ±.014 .920 ±.010 .933 ±.010 .933 ±.017 .931 ±.018 .925 ±.018 .928 ±.011 .926 ±.014 .935 ±.008 .925 ±.006 .929 ±.012 .931 ±.016 .929 ±.021 .933 ±.013 3 .934 ±.015 .926 ±.017 .925 ±.013 .932 ±.013 .930 ±.017 .942 ±.012 .939 ±.012 .937 ±.011 .932 ±.022 .937 ±.016 .924 ±.015 .932 ±.011 .936 ±.013 .931 ±.021 .935 ±.015 4 .936 ±.017 .939 ±.012 .927 ±.013 .932 ±.014 .932 ±.017 .930 ±.019 .938 ±.014 .933 ±.016 .935 ±.016 .938 ±.013 .934 ±.012 .933 ±.016 .930 ±.015 .934 ±.012 .933 ±.018 5 .938 ±.012 .936 ±.016 .927 ±.012 .932 ±.015 .934 ±.017 .936 ±.014 .939 ±.011 .937 ±.010 .934 ±.016 .939 ±.009 .928 ±.017 .935 ±.015 .938 ±.009 .937 ±.012 .934 ±.020 thyroid discordant 1 .977 ±.013 .765 ±.209 .909 ±.051 .965 ±.022 .535 ±.001 .765 ±.209 .968 ±.031 .960 ±.017 .928 ±.030 .884 ±.037 .930 ±.033 .713 ±.224 .986 ±.003 .975 ±.012 .858 ±.173 2 .979 ±.013 .922 ±.042 .923 ±.043 .971 ±.010 .572 ±.002 .922 ±.042 .964 ±.030 .981 ±.004 .928 ±.030 .902 ±.027 .920 ±.038 .710 ±.194 .987 ±.003 .987 ±.005 .858 ±.171 3 .977 ±.011 .942 ±.021 .950 ±.048 .974 ±.011 .560 ±.036 .942 ±.021 .966 ±.030 .982 ±.006 .935 ±.023 .921 ±.025 .931 ±.074 .954 ±.033 .988 ±.003 .983 ±.003 .860 ±.171 4 .977 ±.012 .954 ±.008 .972 ±.027 .976 ±.012 .595 ±.029 .954 ±.008 .963 ±.029 .984 ±.004 .952 ±.035 .935 ±.036 .986 ±.007 .971 ±.021 .978 ±.014 .986 ±.006 .861 ±.170 5 .978 ±.013 .960 ±.010 .986 ±.005 .976 ±.013 .877 ±.124 .960 ±.010 .968 ±.029 .984 ±.003 .973 ±.021 .958 ±.034 .983 ±.007 .985 ±.007 .986 ±.003 .984 ±.003 .951 ±.064 coil_2000 insurance_policies 1 .687 ±.028 .652 ±.048 .700 ±.027 .682 ±.019 .533 ±.008 .608 ±.052 .680 ±.015 .679 ±.025 .682 ±.009 .685 ±.058 .595 ±.018 .642 ±.044 .617 ±.033 .686 ±.028 .682 ±.048 2 .697 ±.016 .681 ±.040 .697 ±.024 .680 ±.020 .556 ±.015 .667 ±.049 .691 ±.020 .679 ±.022 .695 ±.019 .675 ±.044 .591 ±.020 .687 ±.011 .668 ±.037 .683 ±.021 .702 ±.026 3 .701 ±.025 .689 ±.045 .701 ±.025 .678 ±.020 .595 ±.003 .672 ±.046 .683 ±.018 .679 ±.022 .703 ±.022 .666 ±.035 .630 ±.020 .658 ±.015 .655 ±.038 .683 ±.019 .700 ±.025 4 .701 ±.021 .687 ±.042 .700 ±.022 .690 ±.032 .597 ±.007 .674 ±.041 .691 ±.015 .693 ±.022 .699 ±.023 .714 ±.014 .647 ±.018 .702 ±.017 .668 ±.033 .702 ±.024 .688 ±.016 5 .701 ±.016 .694 ±.028 .698 ±.027 .696 ±.026 .597 ±.008 .692 ±.029 .693 ±.022 .700 ±.023 .705 ±.021 .704 ±.025 .648 ±.021 .701 ±.026 .688 ±.037 .701 ±.022 .701 ±.025 prostate cancer 1 .923 ±.082 .940 ±.064 .951 ±.053 .958 ±.073 .466 ±.073 .951 ±.047 .707 ±.072 .951 ±.079 .958 ±.061 .932 ±.088 .959 ±.051 .658 ±.092 .668 ±.111 .939 ±.045 .882 ±.071 2 .945 ±.060 .963 ±.061 .961 ±.059 .960 ±.063 .633 ±.162 .962 ±.063 .773 ±.169 .949 ±.083 .961 ±.065 .963 ±.065 .962 ±.060 .746 ±.172 .800 ±.184 .965 ±.046 .894 ±.022 3 .938 ±.073 .961 ±.062 .958 ±.063 .954 ±.066 .668 ±.068 .956 ±.076 .845 ±.101 .954 ±.059 .961 ±.060 .960 ±.058 .963 ±.059 .868 ±.054 .841 ±.101 .967 ±.045 .882 ±.110 4 .927 ±.062 .961 ±.054 .951 ±.059 .954 ±.053 .779 ±.137 .956 ±.055 .900 ±.075 .938 ±.070 .955 ±.056 .960 ±.061 .950 ±.061 .911 ±.084 .904 ±.070 .959 ±.048 .928 ±.064 5 .937 ±.070 .947 ±.058 .938 ±.060 .945 ±.056 .896 ±.058 .946 ±.066 .909 ±.077 .936 ±.074 .947 ±.063 .955 ±.057 .940 ±.064 .923 ±.063 .926 ±.059 .939 ±.054 .927 ±.066 credit approval 1 .874 ±.018 .893 ±.038 .914 ±.003 .809 ±.009 .637 ±.049 .907 ±.005 .875 ±.015 .827 ±.014 .907 ±.005 .808 ±.010 .865 ±.016 .724 ±.105 .891 ±.032 .892 ±.016 .891 ±.033 2 .906 ±.008 .897 ±.016 .907 ±.023 .833 ±.010 .719 ±.032 .893 ±.026 .907 ±.009 .837 ±.006 .891 ±.014 .842 ±.046 .888 ±.005 .824 ±.115 .906 ±.014 .903 ±.006 .901 ±.022 3 .917 ±.013 .914 ±.015 .913 ±.003 .859 ±.007 .890 ±.012 .910 ±.012 .922 ±.006 .852 ±.004 .902 ±.006 .885 ±.032 .918 ±.007 .856 ±.058 .914 ±.015 .925 ±.010 .901 ±.004 4 .929 ±.005 .923 ±.003 .926 ±.008 .881 ±.038 .914 ±.004 .928 ±.009 .924 ±.004 .859 ±.009 .930 ±.006 .920 ±.006 .921 ±.010 .870 ±.044 .929 ±.003 .927 ±.004 .917 ±.008 5 .929 ±.004 .932 ±.004 .927 ±.007 .930 ±.006 .923 ±.006 .929 ±.004 .924 ±.006 .912 ±.035 .931 ±.006 .934 ±.009 .929 ±.004 .887 ±.046 .927 ±.003 .929 ±.005 .926 ±.005 heart_disease hungary 1 .752 ±.054 .872 ±.019 .861 ±.023 .820 ±.081 .701 ±.121 .871 ±.019 .699 ±.215 .829 ±.024 .812 ±.066 .831 ±.031 .855 ±.066 .704 ±.125 .827 ±.050 .843 ±.048 .845 ±.034 2 .793 ±.060 .876 ±.035 .851 ±.035 .847 ±.010 .882 ±.038 .859 ±.044 .703 ±.192 .848 ±.025 .849 ±.025 .855 ±.022 .877 ±.019 .810 ±.067 .837 ±.022 .819 ±.003 .854 ±.040 3 .806 ±.046 .864 ±.030 .866 ±.026 .850 ±.031 .838 ±.059 .862 ±.026 .803 ±.099 .847 ±.019 .868 ±.027 .861 ±.028 .877 ±.025 .801 ±.073 .872 ±.044 .843 ±.032 .863 ±.056 4 .855 ±.029 .875 ±.031 .859 ±.039 .837 ±.041 .876 ±.029 .861 ±.051 .841 ±.082 .842 ±.041 .875 ±.051 .873 ±.044 .865 ±.021 .860 ±.022 .886 ±.010 .852 ±.039 .897 ±.008 5 .862 ±.036 .876 ±.029 .869 ±.043 .856 ±.027 .869 ±.030 .870 ±.039 .850 ±.080 .859 ±.039 .875 ±.037 .877 ±.044 .866 ±.021 .860 ±.038 .869 ±.034 .862 ±.038 .866 ±.034 give_me some_credit 1 .563 ±.002 .732 ±.001 .732 ±.005 .732 ±.005 .607 ±.021 .732 ±.001 .755 ±.003 .732 ±.005 .759 ±.003 .732 ±.005 .625 ±.005 .701 ±.035 .749 ±.014 .563 ±.002 .759 ±.003 2 .718 ±.005 .741 ±.044 .786 ±.004 .742 ±.005 .683 ±.036 .741 ±.044 .741 ±.008 .720 ±.023 .793 ±.004 .783 ±.004 .620 ±.004 .722 ±.016 .727 ±.016 .640 ±.006 .794 ±.003 3 .732 ±.003 .755 ±.025 .790 ±.006 .697 ±.005 .706 ±.087 .755 ±.025 .761 ±.006 .725 ±.004 .790 ±.006 .790 ±.006 .697 ±.004 .720 ±.037 .770 ±.001 .731 ±.004 .804 ±.005 4 .785 ±.014 .758 ±.003 .757 ±.002 .740 ±.006 .736 ±.016 .758 ±.003 .798 ±.011 .762 ±.005 .761 ±.001 .758 ±.003 .751 ±.013 .765 ±.016 .802 ±.006 .802 ±.009 .753 ±.005 5 .814 ±.001 .798 ±.001 .806 ±.009 .798 ±.007 .801 ±.013 .798 ±.001 .813 ±.001 .794 ±.006 .812 ±.002 .797 ±.002 .804 ±.006 .811 ±.003 .813 ±.002 .814 ±.002 .804 ±.008 clock_protein toxicity 1 .482 ±.083 .483 ±.069 .475 ±.027 .570 ±.069 .519 ±.097 .483 ±.069 .451 ±.040 .407 ±.087 .503 ±.126 .491 ±.131 .455 ±.130 .514 ±.102 .440 ±.076 .521 ±.125 .513 ±.076 2 .481 ±.035 .521 ±.089 .522 ±.048 .519 ±.098 .535 ±.044 .521 ±.089 .472 ±.043 .481 ±.029 .510 ±.129 .525 ±.108 .490 ±.091 .478 ±.016 .527 ±.073 .528 ±.068 .491 ±.112 3 .471 ±.039 .512 ±.018 .512 ±.023 .574 ±.117 .510 ±.032 .512 ±.018 .463 ±.067 .527 ±.059 .497 ±.156 .504 ±.090 .512 ±.131 .442 ±.024 .489 ±.066 .507 ±.083 .525 ±.072 4 .497 ±.073 .462 ±.058 .501 ±.063 .531 ±.083 .474 ±.051 .462 ±.058 .461 ±.045 .552 ±.122 .513 ±.112 .520 ±.110 .495 ±.032 .499 ±.135 .535 ±.134 .493 ±.082 .494 ±.032 5 .455 ±.077 .444 ±.123 .454 ±.048 .477 ±.073 .499 ±.066 .444 ±.123 .443 ±.053 .490 ±.088 .494 ±.067 .500 ±.061 .513 ±.052 .447 ±.084 .454 ±.098 .463 ±.064 .492 ±.089 online_shoppers_purchasing intention_dataset 1 .870 ±.002 .879 ±.007 .870 ±.005 .861 ±.010 .649 ±.008 .879 ±.007 .897 ±.004 .872 ±.007 .880 ±.007 .889 ±.009 .660 ±.012 .652 ±.016 .881 ±.005 .880 ±.007 .829 ±.010 2 .897 ±.007 .915 ±.006 .897 ±.007 .900 ±.021 .644 ±.006 .914 ±.005 .911 ±.013 .918 ±.003 .904 ±.018 .892 ±.007 .832 ±.127 .668 ±.050 .919 ±.005 .897 ±.006 .910 ±.007 3 .925 ±.005 .923 ±.005 .924 ±.006 .923 ±.005 .720 ±.002 .923 ±.005 .918 ±.010 .924 ±.003 .925 ±.004 .904 ±.005 .916 ±.007 .898 ±.007 .924 ±.007 .925 ±.005 .925 ±.006 4 .925 ±.004 .924 ±.005 .924 ±.006 .925 ±.005 .924 ±.006 .926 ±.005 .924 ±.004 .925 ±.004 .926 ±.005 .925 ±.006 .925 ±.006 .920 ±.008 .926 ±.004 .925 ±.005 .926 ±.006 5 .925 ±.005 .926 ±.005 .926 ±.007 .925 ±.004 .924 ±.006 .927 ±.006 .927 ±.004 .926 ±.006 .925 ±.006 .926 ±.005 .926 ±.005 .864 ±.088 .926 ±.006 .926 ±.005 .926 ±.007 credit_card clients_default 1 .705 ±.003 .708 ±.005 .738 ±.002 .738 ±.002 .680 ±.003 .707 ±.003 .708 ±.010 .717 ±.012 .738 ±.002 .721 ±.002 .683 ±.003 .645 ±.026 .714 ±.002 .707 ±.005 .727 ±.006 2 .751 ±.003 .740 ±.009 .721 ±.005 .732 ±.005 .679 ±.002 .737 ±.007 .744 ±.006 .732 ±.005 .723 ±.004 .727 ±.004 .745 ±.004 .707 ±.047 .747 ±.004 .742 ±.005 .736 ±.010 3 .756 ±.004 .750 ±.010 .754 ±.006 .753 ±.007 .727 ±.024 .750 ±.010 .760 ±.009 .756 ±.007 .754 ±.002 .760 ±.004 .758 ±.005 .747 ±.020 .758 ±.006 .754 ±.003 .753 ±.003 4 .759 ±.004 .761 ±.004 .757 ±.006 .759 ±.005 .748 ±.008 .758 ±.005 .762 ±.006 .761 ±.008 .757 ±.005 .762 ±.004 .762 ±.005 .749 ±.020 .763 ±.009 .759 ±.003 .760 ±.005 5 .766 ±.004 .768 ±.004 .766 ±.006 .766 ±.005 .767 ±.004 .766 ±.002 .763 ±.007 .764 ±.009 .760 ±.006 .769 ±.004 .764 ±.007 .754 ±.026 .764 ±.009 .764 ±.005 .762 ±.006 bioresponse 1 .788 ±.020 .751 ±.017 .770 ±.007 .669 ±.016 .508 ±.002 .740 ±.028 .603 ±.035 .768 ±.034 .804 ±.011 .713 ±.030 .762 ±.004 .534 ±.012 .542 ±.017 .768 ±.025 .746 ±.025 2 .839 ±.018 .777 ±.007 .813 ±.014 .717 ±.016 .513 ±.008 .791 ±.008 .711 ±.054 .837 ±.029 .846 ±.014 .761 ±.021 .793 ±.008 .709 ±.033 .662 ±.065 .846 ±.022 .779 ±.012 3 .848 ±.022 .852 ±.012 .852 ±.021 .832 ±.019 .515 ±.004 .847 ±.012 .821 ±.015 .849 ±.024 .855 ±.017 .821 ±.015 .840 ±.009 .773 ±.050 .732 ±.006 .856 ±.019 .818 ±.017 4 .860 ±.018 .863 ±.014 .861 ±.020 .853 ±.025 .519 ±.006 .862 ±.011 .837 ±.025 .855 ±.024 .861 ±.018 .854 ±.019 .849 ±.010 .834 ±.029 .832 ±.033 .861 ±.021 .853 ±.012 5 .865 ±.016 .869 ±.019 .866 ±.018 .861 ±.022 .770 ±.010 .868 ±.013 .858 ±.023 .863 ±.023 .864 ±.019 .858 ±.022 .864 ±.020 .859 ±.021 .855 ±.022 .868 ±.018 .863 ±.019 64 (continued from previous page) DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCO MarkovBlanket MImRMR(R)ReliefFRandomSBERFImportanceSFS lymphography 1 .858 ±.083 .815 ±.105 .822 ±.066 .777 ±.060 .630 ±.062 .819 ±.108 .821 ±.108 .784 ±.020 .858 ±.028 .852 ±.030 .857 ±.054 .727 ±.140 .836 ±.065 .868 ±.016 .834 ±.073 2 .916 ±.029 .855 ±.072 .910 ±.014 .851 ±.061 .685 ±.162 .833 ±.075 .871 ±.054 .797 ±.033 .897 ±.045 .861 ±.046 .915 ±.013 .720 ±.235 .899 ±.045 .934 ±.023 .864 ±.049 3 .929 ±.015 .875 ±.077 .913 ±.036 .886 ±.028 .825 ±.128 .879 ±.042 .864 ±.110 .857 ±.039 .905 ±.067 .853 ±.027 .907 ±.041 .835 ±.009 .916 ±.046 .933 ±.003 .847 ±.088 4 .936 ±.010 .908 ±.042 .919 ±.015 .891 ±.034 .918 ±.056 .895 ±.025 .883 ±.052 .899 ±.041 .903 ±.033 .865 ±.051 .918 ±.036 .878 ±.045 .913 ±.052 .928 ±.026 .872 ±.101 5 .935 ±.028 .925 ±.043 .923 ±.034 .892 ±.038 .904 ±.056 .900 ±.035 .892 ±.057 .904 ±.038 .897 ±.038 .899 ±.093 .934 ±.021 .922 ±.050 .905 ±.054 .919 ±.048 .908 ±.096 nci_ovarian cancer 1 .987 ±.019 .993 ±.008 .979 ±.019 .985 ±.020 .592 ±.081 1.00 ±.00 .804 ±.080 .980 ±.033 .979 ±.019 .998 ±.002 .979 ±.019 .782 ±.184 .781 ±.094 .996 ±.006 .987 ±.011 2 .994 ±.011 .999 ±.001 .998 ±.002 .998 ±.002 .724 ±.114 1.00 ±.00 .946 ±.044 1.00 ±.00 .998 ±.002 .999 ±.001 .997 ±.003 .936 ±.045 .936 ±.084 .998 ±.002 .998 ±.002 3 .999 ±.002 1.00 ±.00 .999 ±.001 .999 ±.001 .770 ±.125 1.00 ±.00 .985 ±.016 .999 ±.001 .999 ±.001 .999 ±.001 .999 ±.001 .986 ±.017 .988 ±.013 .999 ±.001 .996 ±.004 4 .999 ±.002 1.00 ±.00 .999 ±.000 .999 ±.001 .891 ±.011 1.00 ±.00 .993 ±.009 1.00 ±.00 .999 ±.001 1.00 ±.00 1.00 ±.00 .997 ±.004 .997 ±.005 1.00 ±.00 .999 ±.002 5 .999 ±.002 1.00 ±.00 1.00 ±.00 1.00 ±.00 .929 ±.035 1.00 ±.00 .999 ±.001 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 .998 ±.003 .998 ±.003 1.00 ±.00 .998 ±.004 bad_customer detection 1 .550 ±.024 .594 ±.055 .596 ±.036 .689 ±.036 .648 ±.041 .594 ±.055 .592 ±.055 .602 ±.059 .646 ±.054 .666 ±.022 .610 ±.040 .581 ±.032 .661 ±.042 .555 ±.031 .676 ±.010 2 .671 ±.022 .560 ±.042 .612 ±.013 .695 ±.002 .627 ±.027 .560 ±.042 .617 ±.083 .661 ±.030 .685 ±.033 .640 ±.017 .601 ±.016 .583 ±.033 .668 ±.020 .673 ±.022 .672 ±.006 3 .706 ±.031 .622 ±.015 .607 ±.018 .714 ±.018 .606 ±.021 .622 ±.015 .666 ±.014 .724 ±.016 .718 ±.016 .658 ±.043 .558 ±.024 .651 ±.079 .663 ±.048 .698 ±.015 .645 ±.024 4 .729 ±.013 .642 ±.019 .639 ±.046 .730 ±.016 .647 ±.031 .642 ±.019 .677 ±.015 .723 ±.019 .723 ±.024 .668 ±.033 .620 ±.016 .694 ±.053 .675 ±.044 .723 ±.014 .662 ±.013 5 .730 ±.005 .705 ±.043 .709 ±.016 .748 ±.017 .688 ±.034 .705 ±.043 .711 ±.012 .728 ±.002 .732 ±.023 .716 ±.022 .676 ±.023 .736 ±.030 .713 ±.002 .746 ±.014 .707 ±.014 hazelnut_spread contaminant_detection_10GHz 1 .931 ±.020 .917 ±.019 .915 ±.011 .919 ±.012 .890 ±.013 .914 ±.020 .924 ±.023 .922 ±.016 .917 ±.013 .917 ±.020 .926 ±.012 .864 ±.005 .927 ±.011 .927 ±.019 .915 ±.014 2 .952 ±.018 .942 ±.014 .944 ±.012 .943 ±.014 .916 ±.015 .941 ±.014 .954 ±.015 .946 ±.014 .944 ±.014 .941 ±.010 .947 ±.009 .918 ±.025 .953 ±.013 .946 ±.014 .947 ±.020 3 .958 ±.012 .949 ±.013 .956 ±.014 .947 ±.014 .932 ±.018 .951 ±.012 .955 ±.013 .956 ±.015 .948 ±.015 .953 ±.008 .957 ±.011 .941 ±.024 .958 ±.013 .953 ±.015 .956 ±.015 4 .958 ±.012 .954 ±.014 .955 ±.012 .954 ±.015 .939 ±.017 .954 ±.015 .957 ±.015 .956 ±.015 .954 ±.019 .955 ±.011 .958 ±.012 .952 ±.019 .958 ±.015 .958 ±.015 .956 ±.013 5 .958 ±.012 .958 ±.014 .956 ±.012 .956 ±.014 .945 ±.015 .958 ±.014 .957 ±.014 .957 ±.012 .956 ±.016 .957 ±.014 .959 ±.012 .953 ±.016 .957 ±.014 .957 ±.014 .956 ±.012 drug_induced autoimmunity_prediction 1 .684 ±.051 .631 ±.066 .622 ±.067 .628 ±.068 .583 ±.077 .610 ±.137 .677 ±.018 .711 ±.020 .679 ±.049 .672 ±.015 .581 ±.034 .638 ±.126 .604 ±.065 .704 ±.019 .674 ±.070 2 .721 ±.037 .697 ±.065 .626 ±.045 .728 ±.046 .619 ±.039 .703 ±.035 .750 ±.021 .738 ±.042 .702 ±.025 .705 ±.042 .661 ±.026 .723 ±.038 .731 ±.015 .714 ±.030 .713 ±.080 3 .769 ±.021 .769 ±.012 .788 ±.001 .797 ±.014 .634 ±.030 .773 ±.052 .774 ±.030 .794 ±.030 .739 ±.029 .774 ±.053 .778 ±.031 .741 ±.022 .770 ±.017 .781 ±.002 .767 ±.014 4 .802 ±.010 .797 ±.013 .813 ±.017 .799 ±.003 .721 ±.010 .798 ±.007 .784 ±.016 .802 ±.016 .788 ±.033 .807 ±.021 .773 ±.004 .754 ±.031 .771 ±.022 .787 ±.020 .796 ±.030 5 .816 ±.014 .802 ±.016 .819 ±.009 .798 ±.005 .784 ±.024 .807 ±.004 .792 ±.005 .810 ±.038 .786 ±.030 .796 ±.006 .803 ±.004 .790 ±.010 .797 ±.008 .803 ±.012 .810 ±.014 heart_disease switzerland 1 .414 ±.160 .412 ±.089 .734 ±.226 .521 ±.077 .726 ±.149 .412 ±.089 .481 ±.139 .598 ±.350 .701 ±.142 .530 ±.140 .845 ±.137 .467 ±.373 .391 ±.125 .462 ±.222 .372 ±.078 2 .558 ±.198 .362 ±.040 .808 ±.111 .621 ±.142 .731 ±.094 .362 ±.040 .501 ±.119 .556 ±.353 .788 ±.151 .576 ±.191 .823 ±.120 .504 ±.156 .543 ±.191 .652 ±.120 .339 ±.086 3 .670 ±.140 .316 ±.100 .680 ±.153 .437 ±.137 .433 ±.067 .316 ±.100 .527 ±.113 .437 ±.098 .788 ±.169 .775 ±.191 .751 ±.130 .356 ±.136 .617 ±.276 .499 ±.152 .580 ±.275 4 .629 ±.205 .388 ±.225 .643 ±.176 .435 ±.149 .456 ±.046 .388 ±.225 .751 ±.153 .396 ±.118 .805 ±.126 .667 ±.233 .677 ±.169 .563 ±.249 .654 ±.213 .568 ±.120 .682 ±.176 5 .649 ±.205 .582 ±.193 .595 ±.199 .430 ±.159 .440 ±.120 .582 ±.193 .682 ±.176 .577 ±.167 .641 ±.247 .633 ±.215 .622 ±.166 .669 ±.222 .681 ±.166 .626 ±.180 .655 ±.199 hr analytics 1 .662 ±.009 .771 ±.008 .771 ±.008 .758 ±.023 .645 ±.006 .771 ±.008 .682 ±.002 .771 ±.008 .717 ±.005 .771 ±.008 .660 ±.013 .581 ±.045 .769 ±.008 .662 ±.009 .771 ±.009 2 .738 ±.001 .772 ±.005 .768 ±.005 .762 ±.009 .655 ±.010 .772 ±.005 .642 ±.023 .767 ±.008 .770 ±.007 .771 ±.007 .702 ±.040 .687 ±.011 .764 ±.016 .659 ±.003 .772 ±.006 3 .760 ±.001 .756 ±.010 .762 ±.009 .754 ±.007 .664 ±.006 .756 ±.010 .682 ±.068 .762 ±.002 .753 ±.008 .743 ±.010 .744 ±.020 .752 ±.009 .762 ±.005 .759 ±.002 .766 ±.005 4 .776 ±.002 .761 ±.020 .764 ±.007 .765 ±.004 .652 ±.009 .761 ±.020 .723 ±.060 .765 ±.003 .772 ±.005 .772 ±.007 .755 ±.013 .756 ±.009 .767 ±.008 .772 ±.003 .766 ±.005 5 .784 ±.005 .781 ±.006 .777 ±.010 .787 ±.004 .776 ±.003 .781 ±.006 .781 ±.008 .787 ±.005 .779 ±.008 .784 ±.004 .779 ±.012 .779 ±.012 .781 ±.008 .785 ±.005 .784 ±.005 porto seguro 1 .577 ±.003 .560 ±.009 .553 ±.005 .545 ±.006 .548 ±.002 .559 ±.017 .561 ±.028 .560 ±.006 .565 ±.016 .536 ±.005 .564 ±.002 .526 ±.020 .556 ±.025 .575 ±.001 .576 ±.011 2 .586 ±.002 .597 ±.009 .583 ±.005 .592 ±.001 .590 ±.002 .585 ±.019 .580 ±.014 .595 ±.005 .583 ±.004 .552 ±.005 .586 ±.004 .565 ±.018 .569 ±.013 .587 ±.002 .542 ±.018 3 .590 ±.002 .606 ±.007 .603 ±.002 .600 ±.001 .561 ±.001 .598 ±.008 .592 ±.012 .596 ±.005 .599 ±.001 .562 ±.004 .592 ±.001 .585 ±.003 .593 ±.010 .598 ±.002 .582 ±.006 4 .606 ±.001 .611 ±.005 .603 ±.001 .603 ±.004 .575 ±.009 .602 ±.008 .602 ±.008 .601 ±.002 .605 ±.003 .593 ±.000 .597 ±.001 .602 ±.002 .600 ±.006 .605 ±.001 .597 ±.004 5 .609 ±.003 .611 ±.003 .610 ±.003 .609 ±.002 .601 ±.002 .608 ±.006 .607 ±.001 .607 ±.004 .611 ±.001 .610 ±.003 .607 ±.003 .610 ±.001 .607 ±.003 .610 ±.001 .603 ±.002 japanese_credit screening 1 .734 ±.079 .615 ±.171 .760 ±.013 .732 ±.019 .687 ±.143 .729 ±.076 .645 ±.139 .737 ±.026 .717 ±.038 .733 ±.032 .735 ±.056 .579 ±.053 .707 ±.033 .739 ±.025 .704 ±.034 2 .741 ±.026 .725 ±.060 .725 ±.051 .735 ±.038 .700 ±.111 .725 ±.071 .741 ±.053 .729 ±.023 .734 ±.026 .772 ±.093 .763 ±.015 .709 ±.102 .711 ±.041 .713 ±.039 .721 ±.020 3 .766 ±.027 .769 ±.011 .753 ±.075 .729 ±.033 .667 ±.097 .748 ±.027 .747 ±.053 .738 ±.034 .748 ±.032 .753 ±.089 .770 ±.010 .668 ±.066 .712 ±.061 .754 ±.010 .766 ±.061 4 .780 ±.017 .781 ±.032 .774 ±.059 .763 ±.027 .771 ±.081 .767 ±.018 .788 ±.021 .759 ±.015 .765 ±.006 .792 ±.082 .761 ±.018 .767 ±.043 .776 ±.030 .756 ±.017 .758 ±.034 5 .789 ±.032 .766 ±.031 .792 ±.034 .780 ±.016 .777 ±.062 .784 ±.031 .788 ±.034 .792 ±.042 .779 ±.026 .797 ±.035 .783 ±.027 .768 ±.025 .777 ±.049 .780 ±.034 .788 ±.023 ionosphere 1 .967 ±.012 .934 ±.038 .958 ±.014 .963 ±.020 .925 ±.040 .926 ±.049 .954 ±.032 .968 ±.020 .958 ±.031 .954 ±.034 .954 ±.022 .941 ±.009 .953 ±.027 .967 ±.007 .962 ±.010 2 .968 ±.018 .962 ±.025 .973 ±.020 .971 ±.021 .952 ±.034 .963 ±.024 .961 ±.030 .968 ±.025 .968 ±.032 .969 ±.018 .971 ±.015 .962 ±.037 .964 ±.034 .970 ±.019 .964 ±.015 3 .970 ±.022 .973 ±.021 .971 ±.019 .970 ±.020 .956 ±.038 .970 ±.025 .961 ±.029 .973 ±.031 .974 ±.022 .971 ±.027 .965 ±.020 .975 ±.019 .967 ±.033 .971 ±.026 .965 ±.022 4 .971 ±.024 .975 ±.022 .976 ±.017 .973 ±.024 .965 ±.022 .973 ±.029 .969 ±.033 .975 ±.023 .972 ±.028 .974 ±.027 .971 ±.022 .971 ±.028 .969 ±.032 .975 ±.022 .971 ±.025 5 .974 ±.022 .974 ±.023 .975 ±.023 .975 ±.020 .970 ±.018 .974 ±.022 .975 ±.025 .976 ±.021 .975 ±.025 .973 ±.029 .974 ±.019 .975 ±.022 .974 ±.021 .975 ±.023 .972 ±.024 smoking lung_cancer 1 .676 ±.040 .686 ±.046 .691 ±.042 .705 ±.065 .664 ±.033 .698 ±.047 .659 ±.096 .686 ±.086 .688 ±.089 .691 ±.084 .724 ±.080 .585 ±.019 .528 ±.077 .714 ±.093 .556 ±.102 2 .750 ±.018 .760 ±.063 .728 ±.071 .742 ±.056 .719 ±.021 .753 ±.041 .679 ±.052 .715 ±.069 .747 ±.043 .738 ±.027 .724 ±.034 .684 ±.046 .675 ±.062 .735 ±.029 .721 ±.027 3 .750 ±.015 .761 ±.058 .730 ±.037 .745 ±.040 .738 ±.025 .763 ±.054 .718 ±.031 .734 ±.034 .750 ±.034 .735 ±.041 .745 ±.059 .703 ±.027 .706 ±.026 .752 ±.040 .742 ±.014 4 .732 ±.018 .760 ±.044 .738 ±.044 .740 ±.042 .763 ±.033 .759 ±.032 .733 ±.057 .740 ±.044 .735 ±.032 .745 ±.043 .743 ±.045 .739 ±.050 .744 ±.042 .753 ±.033 .739 ±.044 5 .749 ±.047 .747 ±.034 .731 ±.057 .738 ±.045 .751 ±.042 .752 ±.022 .740 ±.027 .739 ±.052 .734 ±.020 .733 ±.025 .751 ±.037 .757 ±.042 .743 ±.030 .753 ±.029 .740 ±.033 nci_pancreatic cancer 1 .605 ±.070 .623 ±.023 .570 ±.130 .547 ±.052 .518 ±.116 .616 ±.097 .562 ±.029 .525 ±.065 .592 ±.094 .563 ±.067 .565 ±.053 .478 ±.056 .462 ±.026 .566 ±.063 .523 ±.115 2 .565 ±.027 .724 ±.038 .646 ±.052 .568 ±.037 .529 ±.089 .711 ±.013 .554 ±.017 .611 ±.036 .570 ±.018 .650 ±.037 .622 ±.025 .552 ±.028 .584 ±.033 .657 ±.015 .540 ±.047 3 .601 ±.035 .688 ±.032 .636 ±.015 .574 ±.057 .517 ±.053 .665 ±.038 .571 ±.055 .572 ±.039 .557 ±.031 .599 ±.014 .599 ±.010 .584 ±.001 .589 ±.046 .644 ±.033 .631 ±.062 4 .575 ±.014 .676 ±.026 .617 ±.018 .559 ±.039 .546 ±.050 .624 ±.021 .570 ±.022 .571 ±.061 .553 ±.025 .585 ±.008 .596 ±.015 .569 ±.019 .565 ±.023 .602 ±.019 .572 ±.017 5 .575 ±.038 .606 ±.033 .598 ±.011 .561 ±.049 .526 ±.034 .601 ±.011 .564 ±.023 .567 ±.029 .566 ±.011 .590 ±.032 .593 ±.018 .554 ±.025 .593 ±.034 .611 ±.028 .585 ±.034 homesite_quote conversion 1 .902 ±.001 .816 ±.002 .875 ±.001 .605 ±.143 .502 ±.000 .816 ±.002 .799 ±.045 .875 ±.020 .890 ±.001 .897 ±.001 .862 ±.037 .596 ±.036 .602 ±.022 .903 ±.001 .853 ±.009 2 .931 ±.006 .929 ±.001 .865 ±.008 .868 ±.026 .610 ±.091 .929 ±.001 .855 ±.048 .901 ±.000 .899 ±.001 .888 ±.001 .896 ±.014 .696 ±.064 .700 ±.050 .911 ±.002 .849 ±.014 3 .947 ±.006 .943 ±.001 .902 ±.001 .894 ±.004 .674 ±.002 .942 ±.000 .905 ±.038 .926 ±.001 .916 ±.002 .915 ±.001 .931 ±.002 .757 ±.063 .764 ±.063 .948 ±.001 .897 ±.003 4 .956 ±.003 .958 ±.000 .948 ±.001 .924 ±.000 .674 ±.003 .955 ±.003 .915 ±.013 .927 ±.003 .933 ±.001 .933 ±.005 .936 ±.003 .858 ±.028 .843 ±.014 .955 ±.001 .895 ±.010 5 .957 ±.000 .959 ±.001 .953 ±.001 .930 ±.001 .729 ±.008 .959 ±.001 .913 ±.012 .930 ±.004 .958 ±.001 .959 ±.000 .951 ±.000 .933 ±.016 .905 ±.029 .954 ±.001 .932 ±.012 heart_disease va_long_beach 1 .546 ±.057 .548 ±.081 .632 ±.100 .574 ±.050 .682 ±.095 .550 ±.082 .612 ±.082 .631 ±.031 .567 ±.079 .537 ±.074 .642 ±.002 .579 ±.155 .561 ±.053 .530 ±.063 .564 ±.099 2 .578 ±.116 .599 ±.063 .643 ±.063 .640 ±.099 .599 ±.101 .598 ±.063 .635 ±.062 .579 ±.094 .593 ±.046 .538 ±.092 .629 ±.049 .613 ±.121 .708 ±.091 .621 ±.044 .540 ±.054 3 .627 ±.058 .623 ±.086 .637 ±.053 .659 ±.061 .644 ±.112 .614 ±.071 .637 ±.089 .657 ±.080 .667 ±.051 .607 ±.078 .637 ±.073 .662 ±.088 .732 ±.046 .657 ±.044 .609 ±.103 4 .681 ±.073 .626 ±.097 .636 ±.042 .706 ±.077 .692 ±.094 .639 ±.114 .700 ±.064 .687 ±.085 .690 ±.100 .633 ±.104 .697 ±.091 .673 ±.090 .725 ±.081 .705 ±.095 .679 ±.072 5 .712 ±.103 .644 ±.093 .692 ±.083 .704 ±.081 .699 ±.085 .647 ±.097 .736 ±.109 .716 ±.087 .707 ±.100 .710 ±.098 .716 ±.082 .699 ±.090 .729 ±.108 .718 ±.063 .714 ±.087 65 (continued from previous page) DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCO MarkovBlanket MImRMR(R)ReliefFRandomSBERFImportanceSFS qsar_biodeg 1 .908 ±.021 .896 ±.013 .865 ±.018 .869 ±.007 .679 ±.059 .887 ±.005 .872 ±.032 .911 ±.006 .895 ±.023 .902 ±.005 .905 ±.010 .870 ±.030 .897 ±.028 .885 ±.018 .915 ±.019 2 .927 ±.012 .927 ±.005 .914 ±.011 .913 ±.004 .906 ±.007 .919 ±.009 .919 ±.006 .926 ±.010 .919 ±.014 .912 ±.006 .925 ±.011 .906 ±.015 .915 ±.022 .917 ±.016 .920 ±.015 3 .925 ±.015 .927 ±.014 .922 ±.012 .927 ±.011 .914 ±.013 .921 ±.013 .924 ±.008 .927 ±.010 .928 ±.011 .926 ±.010 .923 ±.009 .920 ±.014 .928 ±.007 .926 ±.012 .926 ±.015 4 .929 ±.011 .932 ±.014 .927 ±.012 .930 ±.010 .923 ±.013 .927 ±.015 .927 ±.010 .930 ±.007 .930 ±.009 .933 ±.010 .930 ±.008 .925 ±.016 .929 ±.011 .929 ±.011 .931 ±.009 5 .929 ±.013 .931 ±.014 .929 ±.014 .934 ±.011 .927 ±.012 .930 ±.015 .931 ±.008 .932 ±.010 .932 ±.009 .932 ±.011 .933 ±.013 .928 ±.010 .931 ±.011 .931 ±.010 .933 ±.010 naticusdroid_android permissions_dataset 1 .971 ±.003 .973 ±.003 .972 ±.001 .845 ±.054 .579 ±.002 .967 ±.008 .969 ±.006 .782 ±.092 .969 ±.005 .845 ±.054 .970 ±.001 .883 ±.068 .787 ±.044 .972 ±.001 .969 ±.007 2 .972 ±.003 .975 ±.002 .974 ±.002 .931 ±.001 .633 ±.003 .973 ±.002 .973 ±.003 .869 ±.043 .974 ±.001 .931 ±.001 .970 ±.002 .876 ±.010 .924 ±.019 .974 ±.003 .969 ±.005 3 .975 ±.002 .977 ±.002 .976 ±.002 .950 ±.009 .723 ±.009 .977 ±.002 .974 ±.003 .942 ±.001 .978 ±.002 .950 ±.009 .972 ±.004 .937 ±.005 .925 ±.048 .975 ±.003 .973 ±.002 4 .976 ±.003 .978 ±.002 .977 ±.002 .971 ±.002 .819 ±.002 .977 ±.001 .976 ±.004 .952 ±.004 .976 ±.001 .971 ±.002 .974 ±.003 .947 ±.031 .961 ±.014 .976 ±.003 .975 ±.005 5 .977 ±.003 .977 ±.003 .976 ±.003 .976 ±.001 .947 ±.009 .977 ±.003 .975 ±.003 .971 ±.005 .976 ±.002 .976 ±.001 .976 ±.002 .963 ±.011 .969 ±.012 .977 ±.002 .974 ±.006 framingham heart_study 1 .581 ±.035 .624 ±.042 .612 ±.015 .626 ±.026 .555 ±.016 .618 ±.044 .624 ±.022 .596 ±.022 .611 ±.021 .647 ±.013 .599 ±.016 .543 ±.033 .623 ±.020 .608 ±.023 .665 ±.007 2 .642 ±.019 .659 ±.025 .630 ±.015 .647 ±.027 .578 ±.019 .660 ±.024 .661 ±.016 .626 ±.020 .625 ±.029 .636 ±.008 .639 ±.008 .576 ±.004 .668 ±.020 .656 ±.023 .645 ±.008 3 .678 ±.027 .670 ±.030 .679 ±.021 .676 ±.038 .599 ±.012 .678 ±.022 .681 ±.023 .670 ±.039 .651 ±.027 .639 ±.015 .676 ±.030 .628 ±.034 .687 ±.022 .680 ±.030 .629 ±.008 4 .694 ±.024 .675 ±.022 .690 ±.016 .684 ±.035 .665 ±.027 .676 ±.021 .681 ±.018 .682 ±.034 .670 ±.026 .654 ±.017 .682 ±.035 .681 ±.026 .690 ±.026 .694 ±.032 .655 ±.007 5 .698 ±.031 .687 ±.030 .694 ±.026 .698 ±.030 .688 ±.028 .685 ±.025 .696 ±.027 .691 ±.030 .685 ±.035 .679 ±.026 .692 ±.028 .668 ±.020 .694 ±.024 .697 ±.032 .680 ±.017 Table D.8: Per-cell results with downstream model RealMLP. Tasks are grouped by metric. Each cell is the mean over three seeds, with the sample standard deviation as a subscript. DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCOMarkovBlanketMImRMR(R)ReliefFRandomSBERFImportanceSFS log-loss (↓) horse_colic survival 1 .803 ±.087 .743 ±.093 .740 ±.074 .814 ±.022 .858 ±.076 .743 ±.093 .809 ±.115 .799 ±.026 .785 ±.115 .789 ±.059 .800 ±.072 .850 ±.095 .845 ±.129 .799 ±.031 .877 ±.047 2 .783 ±.026 .757 ±.095 .752 ±.033 .765 ±.058 .852 ±.058 .774 ±.103 .843 ±.043 .749 ±.037 .788 ±.048 .767 ±.053 .744 ±.065 .805 ±.061 .813 ±.111 .780 ±.126 .826 ±.071 3 .802 ±.056 .753 ±.077 .730 ±.032 .765 ±.033 .806 ±.042 .803 ±.131 .796 ±.054 .729 ±.034 .759 ±.105 .761 ±.056 .826 ±.097 .767 ±.015 .800 ±.019 .752 ±.051 .830 ±.065 4 .778 ±.006 .750 ±.021 .778 ±.041 .740 ±.034 .775 ±.087 .774 ±.053 .780 ±.075 .743 ±.053 .748 ±.043 .745 ±.044 .764 ±.027 .801 ±.033 .750 ±.019 .746 ±.020 .800 ±.034 5 .780 ±.030 .765 ±.041 .763 ±.095 .754 ±.013 .736 ±.022 .766 ±.077 .767 ±.073 .794 ±.064 .740 ±.026 .733 ±.035 .755 ±.050 .764 ±.062 .733 ±.038 .752 ±.033 .783 ±.091 srbct prediction 1 1.48 ±.60 .400 ±.246 .496 ±.289 .624 ±.062 1.37 ±.14 .625 ±.472 1.20 ±.03 .842 ±.311 .899 ±.430 .953 ±1.075 .335 ±.191 .845 ±.410 1.10 ±.18 .432 ±.273 1.13 ±.26 2 .772 ±.535 .136 ±.105 .354 ±.276 .336 ±.317 1.07 ±.20 .0384 ±.0306 .506 ±.327 .371 ±.355 .193 ±.234 .373 ±.301 .0460 ±.0364 1.36 ±.13 1.22 ±.11 .0601 ±.0360 .951 ±.176 3 .242 ±.039 .0206 ±.0083 .0364 ±.0364 .0358 ±.0334 .983 ±.691 .0179 ±.0106 .451 ±.188 .0618 ±.0214 .136 ±.204 .0378 ±.0552 .0151 ±.0152 .453 ±.280 .458 ±.229 .0136 ±.0075 .275 ±.087 4 .116 ±.026 .0261 ±.0239 .0202 ±.0121 .0145 ±.0048 .377 ±.187 .0752 ±.0984 .107 ±.042 .0159 ±.0060 .00977 ±.00243 .0239 ±.0088 .0266 ±.0111 .336 ±.168 .215 ±.021 .0317 ±.0427 .223 ±.163 5 .129 ±.083 .0563 ±.0234 .0495 ±.0119 .0577 ±.0065 .139 ±.016 .0837 ±.0701 .133 ±.091 .0420 ±.0057 .0415 ±.0180 .0451 ±.0209 .0717 ±.0146 .0775 ±.0474 .0888 ±.0202 .0518 ±.0174 .0808 ±.0108 sdss_17 1 .112 ±.002 .128 ±.015 .156 ±.002 .158 ±.002 .660 ±.002 .250 ±.239 .112 ±.001 .112 ±.001 .155 ±.002 .173 ±.002 .163 ±.001 .538 ±.394 .112 ±.002 .158 ±.002 .156 ±.003 2 .0852 ±.0015 .0877 ±.0027 .0979 ±.0046 .0884 ±.0007 .494 ±.137 .0859 ±.0019 .0851 ±.0007 .0900 ±.0066 .0972 ±.0014 .159 ±.001 .100 ±.001 .113 ±.013 .0860 ±.0025 .0874 ±.0010 .113 ±.002 3 .0961 ±.0022 .0830 ±.0008 .0922 ±.0092 .0957 ±.0009 .416 ±.022 .0825 ±.0003 .0844 ±.0007 .0964 ±.0019 .0957 ±.0010 .100 ±.001 .104 ±.012 .377 ±.055 .0845 ±.0006 .0918 ±.0083 .0965 ±.0021 4 .0972 ±.0013 .0839 ±.0005 .0933 ±.0029 .0965 ±.0000 .190 ±.131 .0856 ±.0039 .0881 ±.0070 .0970 ±.0019 .0966 ±.0017 .104 ±.003 .0962 ±.0014 .279 ±.147 .0843 ±.0008 .0961 ±.0007 .0966 ±.0007 5 .0966 ±.0020 .0896 ±.0005 .0932 ±.0051 .0972 ±.0011 .0977 ±.0032 .0900 ±.0009 .0960 ±.0007 .0993 ±.0008 .0963 ±.0018 .0968 ±.0023 .0973 ±.0013 .185 ±.149 .0965 ±.0008 .0977 ±.0010 .0958 ±.0006 predict_students_dropout and_academic_success 1 .622 ±.027 .585 ±.013 .634 ±.017 .659 ±.006 .973 ±.018 .584 ±.015 .602 ±.037 .596 ±.007 .622 ±.010 .670 ±.021 .591 ±.015 .687 ±.034 .653 ±.034 .656 ±.004 .668 ±.022 2 .568 ±.006 .560 ±.014 .569 ±.013 .597 ±.011 .880 ±.027 .559 ±.020 .579 ±.017 .585 ±.007 .558 ±.016 .623 ±.009 .568 ±.010 .633 ±.022 .641 ±.040 .565 ±.004 .620 ±.042 3 .553 ±.011 .550 ±.011 .556 ±.023 .587 ±.007 .689 ±.027 .548 ±.006 .566 ±.021 .586 ±.003 .547 ±.011 .596 ±.008 .551 ±.017 .667 ±.024 .583 ±.017 .558 ±.009 .598 ±.022 4 .551 ±.020 .549 ±.009 .549 ±.007 .585 ±.008 .596 ±.020 .548 ±.005 .554 ±.021 .585 ±.003 .550 ±.012 .592 ±.010 .543 ±.015 .588 ±.013 .559 ±.012 .546 ±.013 .573 ±.003 5 .548 ±.008 .547 ±.010 .546 ±.006 .569 ±.018 .550 ±.009 .546 ±.011 .557 ±.023 .578 ±.008 .551 ±.011 .557 ±.006 .547 ±.013 .556 ±.025 .547 ±.018 .544 ±.013 .550 ±.011 ancestry study 1 1.63 ±.08 1.76 ±.10 1.73 ±.08 2.04 ±.14 2.13 ±.03 1.89 ±.25 1.81 ±.12 1.74 ±.08 1.72 ±.07 1.77 ±.06 1.73 ±.07 2.07 ±.13 2.15 ±.01 1.74 ±.07 1.87 ±.07 2 1.64 ±.12 1.70 ±.06 1.69 ±.11 1.86 ±.15 2.12 ±.04 1.84 ±.13 1.80 ±.14 1.77 ±.05 1.68 ±.08 1.83 ±.09 1.69 ±.09 2.13 ±.06 2.13 ±.04 1.79 ±.03 1.87 ±.07 3 1.77 ±.20 1.76 ±.07 1.69 ±.05 1.81 ±.05 2.06 ±.14 1.73 ±.09 1.83 ±.13 1.80 ±.04 1.71 ±.12 1.79 ±.04 1.71 ±.10 2.12 ±.07 2.08 ±.07 1.80 ±.14 1.87 ±.03 4 1.73 ±.09 1.77 ±.07 1.74 ±.08 1.81 ±.09 2.01 ±.03 1.69 ±.05 1.77 ±.08 1.81 ±.11 1.67 ±.05 1.75 ±.05 1.75 ±.16 1.91 ±.21 1.97 ±.13 1.67 ±.06 1.91 ±.10 5 1.63 ±.02 1.68 ±.04 1.56 ±.06 1.74 ±.05 1.69 ±.10 1.66 ±.08 1.66 ±.10 1.75 ±.06 1.58 ±.03 1.69 ±.04 1.70 ±.07 1.95 ±.12 1.69 ±.14 1.64 ±.05 1.72 ±.07 otto_group_product classification_challenge 1 .711 ±.006 .785 ±.025 .910 ±.017 1.19 ±.01 .911 ±.025 .858 ±.003 .922 ±.042 .721 ±.009 .924 ±.017 .820 ±.006 .964 ±.019 1.13 ±.11 1.12 ±.08 .719 ±.008 1.13 ±.14 2 .560 ±.007 .594 ±.008 .656 ±.019 .866 ±.007 .647 ±.015 .615 ±.019 .762 ±.011 .586 ±.005 .650 ±.009 .646 ±.010 .655 ±.006 .837 ±.014 .796 ±.021 .569 ±.006 .810 ±.085 3 .504 ±.007 .522 ±.005 .558 ±.006 .603 ±.002 .531 ±.005 .527 ±.001 .646 ±.019 .511 ±.011 .578 ±.007 .560 ±.003 .543 ±.004 .640 ±.021 .628 ±.034 .516 ±.012 .638 ±.033 4 .481 ±.007 .489 ±.007 .507 ±.003 .539 ±.005 .497 ±.005 .490 ±.007 .584 ±.009 .489 ±.006 .507 ±.005 .503 ±.004 .506 ±.008 .563 ±.019 .569 ±.032 .486 ±.007 .581 ±.038 5 .475 ±.008 .478 ±.010 .482 ±.010 .488 ±.009 .481 ±.009 .477 ±.008 .514 ±.005 .475 ±.011 .482 ±.009 .483 ±.005 .479 ±.006 .506 ±.021 .523 ±.014 .475 ±.009 .516 ±.020 lung_cancer 1 .434 ±.198 .455 ±.110 .519 ±.174 .385 ±.180 .804 ±.096 .585 ±.169 .654 ±.146 .387 ±.131 .459 ±.086 .537 ±.385 .417 ±.132 .745 ±.072 .690 ±.027 .525 ±.425 .569 ±.072 2 .251 ±.070 .263 ±.059 .453 ±.153 .237 ±.076 .765 ±.133 .617 ±.029 .305 ±.095 .244 ±.103 .309 ±.123 .319 ±.110 .227 ±.047 .455 ±.146 .521 ±.177 .284 ±.052 .425 ±.162 3 .222 ±.117 .369 ±.252 .240 ±.074 .176 ±.071 .572 ±.062 .446 ±.225 .228 ±.083 .264 ±.060 .174 ±.043 .284 ±.056 .292 ±.154 .181 ±.046 .228 ±.066 .239 ±.134 .263 ±.106 4 .156 ±.035 .262 ±.114 .249 ±.054 .251 ±.052 .352 ±.023 .235 ±.074 .151 ±.039 .220 ±.102 .170 ±.060 .182 ±.040 .293 ±.079 .161 ±.046 .189 ±.033 .253 ±.025 .148 ±.049 5 .188 ±.084 .149 ±.055 .223 ±.100 .193 ±.061 .148 ±.058 .190 ±.075 .198 ±.037 .215 ±.029 .171 ±.027 .221 ±.086 .254 ±.099 .159 ±.026 .180 ±.024 .218 ±.042 .176 ±.039 soybean large 1 .705 ±.158 1.79 ±.09 1.11 ±.28 1.20 ±.03 1.20 ±.28 1.64 ±.28 1.21 ±.26 .559 ±.004 .645 ±.101 .674 ±.075 1.69 ±.11 1.45 ±.32 1.22 ±.42 .536 ±.086 .785 ±.302 2 .346 ±.066 1.22 ±.06 .797 ±.233 .582 ±.102 .615 ±.418 1.19 ±.09 .761 ±.174 .460 ±.126 .437 ±.043 .572 ±.012 1.23 ±.22 .855 ±.166 .650 ±.046 .408 ±.040 .546 ±.169 3 .381 ±.042 .735 ±.085 .652 ±.209 .425 ±.144 .526 ±.274 .951 ±.219 .707 ±.117 .445 ±.084 .326 ±.116 .429 ±.084 .589 ±.112 .406 ±.053 .489 ±.080 .376 ±.099 .447 ±.168 4 .334 ±.088 .472 ±.149 .478 ±.192 .391 ±.276 .443 ±.257 .595 ±.115 .461 ±.127 .419 ±.222 .336 ±.109 .422 ±.126 .466 ±.175 .404 ±.085 .401 ±.131 .338 ±.085 .380 ±.100 5 .326 ±.119 .330 ±.077 .371 ±.125 .317 ±.106 .356 ±.219 .529 ±.057 .357 ±.116 .370 ±.153 .354 ±.159 .333 ±.137 .413 ±.178 .408 ±.153 .385 ±.126 .361 ±.120 .397 ±.185 66 (continued from previous page) DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCOMarkovBlanketMImRMR(R)ReliefFRandomSBERFImportanceSFS mic 1 .512 ±.017 .538 ±.021 .542 ±.032 .562 ±.025 .696 ±.016 .536 ±.036 .600 ±.083 .518 ±.009 .548 ±.025 .520 ±.043 .548 ±.024 .694 ±.010 .687 ±.029 .545 ±.043 .542 ±.017 2 .504 ±.027 .516 ±.029 .504 ±.021 .560 ±.020 .700 ±.027 .520 ±.040 .609 ±.083 .518 ±.016 .556 ±.026 .501 ±.044 .560 ±.024 .698 ±.019 .682 ±.020 .524 ±.035 .550 ±.028 3 .504 ±.016 .498 ±.014 .508 ±.022 .549 ±.017 .698 ±.034 .501 ±.029 .570 ±.083 .506 ±.008 .499 ±.008 .490 ±.020 .550 ±.029 .564 ±.017 .526 ±.009 .490 ±.034 .517 ±.017 4 .519 ±.013 .487 ±.017 .507 ±.023 .528 ±.028 .689 ±.005 .507 ±.023 .547 ±.036 .483 ±.014 .480 ±.029 .481 ±.028 .528 ±.014 .542 ±.079 .532 ±.047 .495 ±.013 .527 ±.032 5 .474 ±.013 .474 ±.015 .472 ±.001 .491 ±.023 .662 ±.021 .486 ±.018 .482 ±.009 .475 ±.012 .481 ±.020 .490 ±.016 .495 ±.026 .503 ±.016 .503 ±.033 .468 ±.019 .490 ±.004 ljubljana primary_tumor 1 1.81 ±.18 2.00 ±.16 1.90 ±.06 1.80 ±.08 2.07 ±.02 1.90 ±.10 1.89 ±.20 1.91 ±.12 1.80 ±.11 1.86 ±.15 1.95 ±.15 1.91 ±.14 1.87 ±.16 1.87 ±.15 1.88 ±.11 2 1.69 ±.17 1.83 ±.24 1.73 ±.11 1.63 ±.20 1.90 ±.12 1.78 ±.20 1.87 ±.21 1.69 ±.19 1.61 ±.18 1.68 ±.20 1.73 ±.14 1.88 ±.10 1.77 ±.21 1.74 ±.16 1.81 ±.19 3 1.51 ±.18 1.69 ±.20 1.57 ±.13 1.60 ±.20 1.82 ±.12 1.66 ±.25 1.61 ±.04 1.54 ±.22 1.49 ±.10 1.63 ±.21 1.59 ±.13 1.73 ±.24 1.61 ±.08 1.54 ±.25 1.66 ±.17 4 1.43 ±.16 1.58 ±.15 1.48 ±.11 1.51 ±.13 1.61 ±.12 1.53 ±.17 1.50 ±.17 1.50 ±.21 1.45 ±.16 1.52 ±.17 1.51 ±.07 1.57 ±.04 1.41 ±.16 1.39 ±.11 1.51 ±.13 5 1.38 ±.13 1.48 ±.12 1.48 ±.13 1.45 ±.11 1.50 ±.09 1.44 ±.14 1.49 ±.15 1.52 ±.25 1.40 ±.12 1.41 ±.14 1.48 ±.14 1.47 ±.19 1.48 ±.13 1.42 ±.09 1.42 ±.11 anneal 1 .0809 ±.0372 .136 ±.053 .228 ±.014 .0853 ±.0163 .525 ±.011 .125 ±.031 .166 ±.034 .0766 ±.0283 .107 ±.032 .126 ±.037 .136 ±.019 .603 ±.128 .112 ±.016 .104 ±.012 .0974 ±.0729 2 .0527 ±.0154 .0565 ±.0219 .104 ±.042 .0940 ±.0342 .426 ±.008 .0519 ±.0145 .0819 ±.0767 .0783 ±.0265 .0876 ±.0382 .0742 ±.0147 .0661 ±.0257 .271 ±.109 .0712 ±.0484 .0469 ±.0155 .0617 ±.0406 3 .0652 ±.0216 .0616 ±.0304 .0495 ±.0275 .0775 ±.0351 .333 ±.037 .0626 ±.0247 .0761 ±.0610 .0592 ±.0253 .0651 ±.0364 .0811 ±.0406 .0599 ±.0240 .161 ±.020 .0424 ±.0205 .0592 ±.0383 .0752 ±.0574 4 .0594 ±.0291 .0622 ±.0361 .0708 ±.0467 .0830 ±.0390 .213 ±.036 .0690 ±.0211 .0507 ±.0179 .0411 ±.0160 .0650 ±.0465 .0709 ±.0422 .0633 ±.0324 .161 ±.085 .0438 ±.0316 .0463 ±.0342 .0789 ±.0529 5 .0483 ±.0290 .0618 ±.0429 .0528 ±.0384 .0610 ±.0242 .0351 ±.0319 .0457 ±.0158 .0446 ±.0187 .0514 ±.0258 .0591 ±.0295 .0573 ±.0049 .0531 ±.0421 .0501 ±.0296 .0450 ±.0286 .0474 ±.0242 .0658 ±.0481 audiology diagnosis 1 .501 ±.273 .610 ±.372 .547 ±.149 .476 ±.235 .548 ±.092 .666 ±.231 .588 ±.145 .512 ±.211 .407 ±.152 .535 ±.129 .598 ±.359 .549 ±.085 .422 ±.016 .457 ±.194 .432 ±.091 2 .504 ±.289 .535 ±.277 .417 ±.102 .482 ±.265 .419 ±.119 .633 ±.152 .557 ±.112 .457 ±.186 .393 ±.132 .495 ±.099 .435 ±.130 .467 ±.204 .431 ±.028 .417 ±.212 .435 ±.161 3 .528 ±.374 .376 ±.126 .345 ±.132 .444 ±.182 .418 ±.087 .570 ±.334 .488 ±.030 .381 ±.174 .378 ±.109 .436 ±.121 .383 ±.129 .426 ±.080 .461 ±.109 .309 ±.129 .432 ±.186 4 .348 ±.126 .365 ±.088 .333 ±.141 .386 ±.189 .396 ±.144 .373 ±.086 .460 ±.043 .384 ±.126 .364 ±.116 .407 ±.129 .374 ±.128 .359 ±.114 .435 ±.017 .322 ±.154 .343 ±.081 5 .335 ±.149 .341 ±.099 .379 ±.156 .384 ±.196 .418 ±.153 .372 ±.119 .433 ±.113 .351 ±.116 .392 ±.156 .394 ±.191 .378 ±.140 .351 ±.090 .434 ±.161 .351 ±.165 .383 ±.116 RMSE (↓) qsar_tid_11 1 1.34 ±.02 1.42 ±.03 1.39 ±.03 1.53 ±.02 1.40 ±.03 1.44 ±.03 1.51 ±.02 1.53 ±.01 1.36 ±.05 1.53 ±.02 1.49 ±.01 1.53 ±.00 1.53 ±.01 1.33 ±.02 1.36 ±.02 2 1.23 ±.01 1.26 ±.04 1.31 ±.03 1.50 ±.01 1.32 ±.00 1.26 ±.04 1.37 ±.01 1.47 ±.05 1.25 ±.02 1.50 ±.01 1.33 ±.06 1.50 ±.02 1.48 ±.03 1.20 ±.02 1.34 ±.04 3 .986 ±.017 1.03 ±.01 1.04 ±.03 1.37 ±.04 1.16 ±.02 1.02 ±.02 1.17 ±.03 1.32 ±.04 1.00 ±.02 1.37 ±.04 1.02 ±.02 1.28 ±.02 1.28 ±.03 .973 ±.022 1.23 ±.03 4 .801 ±.017 .824 ±.036 .835 ±.016 .992 ±.047 .919 ±.006 .822 ±.023 .932 ±.040 .972 ±.014 .831 ±.016 .992 ±.047 .796 ±.016 .935 ±.048 .975 ±.087 .790 ±.035 .909 ±.011 5 .757 ±.027 .759 ±.034 .768 ±.026 .791 ±.004 .774 ±.017 .761 ±.032 .793 ±.028 .799 ±.046 .758 ±.035 .791 ±.004 .746 ±.023 .785 ±.023 .777 ±.031 .747 ±.022 .797 ±.036 forest fires 1 1.42 ±.02 1.40 ±.01 1.42 ±.02 1.40 ±.03 1.41 ±.05 1.40 ±.01 1.42 ±.03 1.41 ±.03 1.41 ±.05 1.40 ±.03 1.40 ±.04 1.41 ±.03 1.40 ±.02 1.42 ±.02 1.40 ±.03 2 1.42 ±.06 1.43 ±.02 1.45 ±.08 1.41 ±.05 1.42 ±.06 1.43 ±.02 1.42 ±.05 1.40 ±.02 1.42 ±.05 1.42 ±.04 1.42 ±.05 1.41 ±.02 1.41 ±.03 1.42 ±.04 1.41 ±.05 3 1.42 ±.04 1.42 ±.02 1.42 ±.03 1.41 ±.06 1.42 ±.07 1.42 ±.02 1.42 ±.05 1.41 ±.03 1.40 ±.03 1.43 ±.07 1.42 ±.07 1.43 ±.04 1.40 ±.03 1.40 ±.05 1.42 ±.07 4 1.42 ±.04 1.42 ±.06 1.43 ±.03 1.42 ±.06 1.42 ±.06 1.42 ±.06 1.41 ±.04 1.45 ±.04 1.42 ±.07 1.41 ±.05 1.43 ±.06 1.40 ±.03 1.41 ±.03 1.43 ±.05 1.43 ±.08 5 1.42 ±.05 1.41 ±.03 1.42 ±.05 1.42 ±.04 1.40 ±.05 1.41 ±.03 1.41 ±.05 1.41 ±.05 1.41 ±.05 1.42 ±.06 1.41 ±.04 1.41 ±.04 1.43 ±.07 1.41 ±.05 1.42 ±.05 allstate claims_severity 1 .670 ±.001 .680 ±.002 .675 ±.002 .705 ±.010 .811 ±.002 .680 ±.002 .669 ±.001 .693 ±.000 .670 ±.002 .716 ±.013 .695 ±.003 .783 ±.024 .786 ±.024 .670 ±.001 .666 ±.005 2 .640 ±.003 .652 ±.002 .658 ±.002 .648 ±.003 .808 ±.003 .652 ±.002 .635 ±.008 .636 ±.001 .645 ±.001 .664 ±.013 .665 ±.022 .788 ±.014 .796 ±.019 .643 ±.005 .652 ±.004 3 .589 ±.002 .605 ±.007 .642 ±.001 .600 ±.011 .807 ±.002 .605 ±.007 .606 ±.008 .615 ±.000 .616 ±.001 .618 ±.001 .632 ±.001 .733 ±.023 .739 ±.015 .589 ±.002 .638 ±.002 4 .558 ±.004 .566 ±.004 .612 ±.008 .570 ±.003 .778 ±.009 .566 ±.004 .565 ±.006 .611 ±.001 .568 ±.002 .604 ±.003 .595 ±.010 .673 ±.036 .673 ±.030 .558 ±.001 .605 ±.022 5 .542 ±.001 .541 ±.001 .555 ±.005 .560 ±.001 .685 ±.010 .541 ±.001 .543 ±.004 .552 ±.001 .544 ±.001 .584 ±.009 .568 ±.009 .599 ±.015 .608 ±.020 .542 ±.001 .578 ±.007 miami housing 1 .245 ±.017 .259 ±.003 .320 ±.002 .272 ±.004 .333 ±.007 .259 ±.003 .345 ±.048 .190 ±.004 .201 ±.025 .314 ±.003 .372 ±.000 .448 ±.092 .196 ±.017 .251 ±.023 .260 ±.006 2 .177 ±.004 .220 ±.004 .237 ±.003 .176 ±.001 .232 ±.001 .220 ±.004 .219 ±.038 .187 ±.002 .186 ±.001 .180 ±.007 .240 ±.023 .300 ±.098 .161 ±.009 .179 ±.002 .169 ±.005 3 .151 ±.011 .168 ±.009 .165 ±.004 .171 ±.001 .226 ±.002 .170 ±.001 .150 ±.008 .177 ±.004 .171 ±.003 .166 ±.004 .168 ±.021 .203 ±.028 .143 ±.004 .151 ±.011 .146 ±.004 4 .148 ±.009 .156 ±.005 .142 ±.004 .161 ±.003 .228 ±.004 .155 ±.005 .141 ±.005 .162 ±.004 .161 ±.003 .150 ±.012 .142 ±.005 .182 ±.034 .141 ±.004 .144 ±.004 .141 ±.004 5 .140 ±.005 .140 ±.006 .140 ±.004 .140 ±.005 .202 ±.002 .140 ±.005 .139 ±.005 .140 ±.004 .139 ±.005 .141 ±.004 .140 ±.004 .158 ±.018 .140 ±.006 .140 ±.004 .140 ±.005 wine quality 1 .735 ±.006 .771 ±.022 .770 ±.006 .770 ±.006 .815 ±.009 .774 ±.021 .827 ±.011 .735 ±.006 .770 ±.006 .774 ±.005 .756 ±.019 .788 ±.033 .758 ±.020 .735 ±.006 .774 ±.005 2 .694 ±.005 .713 ±.006 .724 ±.006 .732 ±.007 .753 ±.003 .715 ±.006 .782 ±.019 .718 ±.005 .745 ±.007 .734 ±.013 .722 ±.012 .768 ±.033 .703 ±.019 .692 ±.008 .709 ±.002 3 .667 ±.018 .704 ±.006 .712 ±.005 .681 ±.017 .699 ±.012 .705 ±.005 .674 ±.031 .679 ±.006 .697 ±.020 .696 ±.010 .652 ±.018 .712 ±.028 .663 ±.013 .669 ±.022 .685 ±.016 4 .629 ±.008 .661 ±.015 .657 ±.001 .638 ±.009 .673 ±.002 .663 ±.014 .634 ±.009 .644 ±.011 .645 ±.021 .647 ±.015 .630 ±.005 .662 ±.015 .630 ±.007 .634 ±.009 .645 ±.008 5 .623 ±.009 .636 ±.014 .640 ±.013 .623 ±.007 .632 ±.011 .634 ±.016 .622 ±.006 .625 ±.014 .627 ±.015 .627 ±.006 .619 ±.013 .631 ±.015 .621 ±.004 .624 ±.009 .634 ±.014 body_density prediction 1 .0110 ±.0006 .0105 ±.0012 .0110 ±.0006 .0108 ±.0009 .0117 ±.0027 .0105 ±.0012 .0121 ±.0023 .0106 ±.0009 .0108 ±.0010 .0110 ±.0013 .0107 ±.0013 .0145 ±.0044 .0110 ±.0014 .0108 ±.0012 .0106 ±.0008 2 .0106 ±.0009 .0106 ±.0012 .0106 ±.0007 .0109 ±.0005 .0108 ±.0005 .0106 ±.0012 .0124 ±.0033 .0109 ±.0004 .0109 ±.0005 .0109 ±.0011 .0105 ±.0013 .0130 ±.0022 .0108 ±.0017 .0106 ±.0014 .0110 ±.0017 3 .0105 ±.0011 .0106 ±.0014 .0110 ±.0008 .0108 ±.0012 .0111 ±.0012 .0106 ±.0014 .0110 ±.0017 .0109 ±.0009 .0108 ±.0008 .0109 ±.0012 .0108 ±.0015 .0117 ±.0023 .0107 ±.0011 .0105 ±.0013 .0109 ±.0011 4 .0106 ±.0011 .0108 ±.0012 .0109 ±.0013 .0112 ±.0012 .0110 ±.0013 .0108 ±.0012 .0108 ±.0013 .0111 ±.0012 .0109 ±.0012 .0107 ±.0013 .0107 ±.0013 .0120 ±.0004 .0108 ±.0010 .0107 ±.0012 .0107 ±.0013 5 .0109 ±.0014 .0107 ±.0013 .0110 ±.0010 .0111 ±.0014 .0106 ±.0012 .0107 ±.0013 .0107 ±.0010 .0112 ±.0014 .0110 ±.0017 .0108 ±.0009 .0105 ±.0011 .0123 ±.0007 .0107 ±.0011 .0105 ±.0013 .0108 ±.0015 brasilian houses ( × 10 3 ) 1 11.7 ±5.8 14.1 ±7.6 11.7 ±5.8 14.5 ±7.9 15.1 ±7.1 14.1 ±7.6 14.5 ±2.3 10.9 ±4.9 14.5 ±7.9 10.9 ±4.9 13.8 ±6.9 12.5 ±6.5 11.3 ±5.3 11.7 ±5.8 15.5 ±4.3 2 12.0 ±5.8 11.0 ±5.1 11.2 ±5.4 10.8 ±5.2 15.4 ±6.6 11.0 ±5.1 12.4 ±6.1 10.6 ±4.6 12.9 ±6.6 13.4 ±7.1 12.0 ±5.7 14.2 ±6.9 11.2 ±5.1 12.0 ±5.8 12.6 ±6.4 3 9.77 ±3.78 11.2 ±5.1 11.7 ±5.5 9.74 ±3.88 13.9 ±6.8 10.4 ±2.2 10.2 ±4.4 9.63 ±3.83 10.2 ±4.2 9.86 ±4.04 7.93 ±2.41 13.1 ±7.1 10.7 ±4.6 8.24 ±3.28 11.8 ±5.8 4 11.0 ±4.9 9.68 ±4.12 8.51 ±2.58 8.50 ±2.67 12.9 ±6.3 9.60 ±1.92 9.78 ±5.53 9.50 ±3.51 9.78 ±4.03 9.04 ±3.34 6.89 ±1.60 11.1 ±4.8 10.1 ±5.1 10.5 ±4.6 8.94 ±3.24 5 8.50 ±2.65 7.66 ±2.34 8.02 ±2.34 8.07 ±2.40 9.60 ±4.12 8.95 ±3.34 8.20 ±2.56 7.68 ±1.86 6.39 ±1.33 9.72 ±3.91 11.2 ±5.1 9.11 ±3.57 8.58 ±3.28 8.80 ±3.56 10.4 ±4.3 automobile 1 .704 ±.123 .719 ±.120 .671 ±.161 .740 ±.097 1.08 ±.10 .722 ±.115 .782 ±.035 .762 ±.087 .549 ±.106 .761 ±.152 .838 ±.180 .794 ±.055 .860 ±.034 .631 ±.131 .856 ±.067 2 .593 ±.104 .759 ±.099 .720 ±.154 .700 ±.112 .890 ±.042 .759 ±.099 .677 ±.060 .748 ±.017 .579 ±.164 .761 ±.128 .809 ±.043 .823 ±.081 .688 ±.079 .585 ±.115 .772 ±.173 3 .572 ±.130 .758 ±.089 .722 ±.133 .688 ±.096 .721 ±.129 .758 ±.089 .661 ±.116 .651 ±.096 .581 ±.107 .679 ±.112 .633 ±.162 .691 ±.111 .666 ±.109 .577 ±.103 .672 ±.099 4 .589 ±.112 .751 ±.088 .616 ±.138 .591 ±.101 .727 ±.100 .751 ±.088 .630 ±.148 .606 ±.097 .583 ±.113 .634 ±.093 .622 ±.162 .663 ±.060 .653 ±.068 .560 ±.081 .646 ±.116 5 .568 ±.090 .693 ±.040 .618 ±.083 .603 ±.117 .667 ±.118 .693 ±.040 .640 ±.114 .597 ±.083 .575 ±.099 .617 ±.101 .579 ±.092 .614 ±.098 .614 ±.130 .593 ±.094 .657 ±.077 pva_revenue prediction_kddcup98 1 .598 ±.002 .597 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .597 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 2 .598 ±.002 .597 ±.002 .597 ±.002 .598 ±.002 .598 ±.002 .597 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 3 .598 ±.002 .597 ±.002 .597 ±.002 .598 ±.002 .598 ±.002 .596 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .598 ±.002 .597 ±.002 4 .598 ±.002 .596 ±.002 .597 ±.002 .598 ±.002 .598 ±.002 .596 ±.002 .597 ±.002 .597 ±.002 .598 ±.002 .597 ±.002 .597 ±.001 .597 ±.002 .597 ±.002 .598 ±.002 .597 ±.002 5 .597 ±.002 .597 ±.002 .597 ±.002 .597 ±.002 .598 ±.002 .597 ±.002 .597 ±.002 .597 ±.002 .597 ±.002 .597 ±.002 .597 ±.002 .597 ±.002 .597 ±.002 .598 ±.002 .597 ±.001 67 (continued from previous page) DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCOMarkovBlanketMImRMR(R)ReliefFRandomSBERFImportanceSFS superconductivity 1 9.85 ±.10 9.86 ±.13 10.4 ±.2 10.7 ±.1 12.0 ±.2 9.93 ±.15 9.78 ±.11 9.86 ±.07 11.8 ±.1 9.75 ±.07 9.88 ±.06 9.86 ±.21 9.94 ±.19 9.82 ±.21 9.97 ±.08 2 9.51 ±.07 9.57 ±.10 9.87 ±.13 9.93 ±.01 10.1 ±.1 9.59 ±.09 9.55 ±.05 9.57 ±.12 10.2 ±.2 9.47 ±.07 9.56 ±.02 9.67 ±.18 9.54 ±.02 9.50 ±.10 9.70 ±.11 3 9.44 ±.08 9.48 ±.14 9.65 ±.06 9.63 ±.06 9.81 ±.05 9.48 ±.10 9.50 ±.08 9.43 ±.15 9.97 ±.14 9.48 ±.06 9.49 ±.05 9.53 ±.11 9.60 ±.19 9.50 ±.08 9.56 ±.13 4 9.39 ±.11 9.46 ±.10 9.50 ±.11 9.52 ±.00 9.60 ±.03 9.41 ±.11 9.42 ±.05 9.39 ±.11 9.57 ±.07 9.44 ±.10 9.43 ±.08 9.51 ±.07 9.49 ±.04 9.48 ±.10 9.49 ±.07 5 9.43 ±.13 9.50 ±.14 9.50 ±.20 9.48 ±.06 9.47 ±.07 9.42 ±.11 9.45 ±.08 9.44 ±.09 9.46 ±.13 9.35 ±.07 9.44 ±.14 9.43 ±.08 9.42 ±.07 9.43 ±.13 9.43 ±.10 AUC (↑) in_vehicle_coupon recommendation 1 .746 ±.010 .718 ±.006 .696 ±.007 .719 ±.012 .580 ±.021 .720 ±.008 .737 ±.009 .729 ±.010 .723 ±.023 .715 ±.016 .745 ±.011 .611 ±.038 .703 ±.057 .746 ±.006 .744 ±.008 2 .792 ±.005 .724 ±.007 .724 ±.005 .761 ±.010 .628 ±.007 .725 ±.008 .796 ±.025 .765 ±.013 .763 ±.009 .740 ±.012 .786 ±.014 .667 ±.018 .808 ±.011 .792 ±.006 .762 ±.019 3 .804 ±.002 .769 ±.007 .738 ±.004 .800 ±.005 .671 ±.002 .768 ±.003 .812 ±.004 .804 ±.003 .801 ±.013 .762 ±.009 .802 ±.005 .734 ±.049 .821 ±.007 .801 ±.004 .795 ±.034 4 .826 ±.007 .803 ±.004 .816 ±.011 .818 ±.004 .724 ±.005 .806 ±.004 .821 ±.005 .815 ±.002 .821 ±.011 .797 ±.006 .815 ±.009 .811 ±.006 .833 ±.004 .832 ±.004 .815 ±.019 5 .834 ±.003 .830 ±.000 .829 ±.007 .829 ±.009 .814 ±.006 .829 ±.002 .832 ±.006 .824 ±.012 .829 ±.012 .827 ±.002 .830 ±.005 .828 ±.001 .832 ±.006 .834 ±.003 .830 ±.005 marketing campaign 1 .820 ±.036 .804 ±.042 .786 ±.012 .774 ±.032 .708 ±.021 .808 ±.037 .783 ±.025 .798 ±.024 .761 ±.011 .811 ±.039 .754 ±.008 .753 ±.026 .820 ±.044 .824 ±.024 .801 ±.058 2 .849 ±.032 .870 ±.034 .873 ±.018 .810 ±.018 .823 ±.021 .873 ±.033 .863 ±.013 .840 ±.010 .836 ±.016 .846 ±.030 .812 ±.046 .778 ±.026 .861 ±.029 .837 ±.022 .850 ±.007 3 .869 ±.016 .889 ±.031 .876 ±.028 .859 ±.014 .852 ±.005 .892 ±.029 .888 ±.024 .857 ±.018 .860 ±.016 .874 ±.026 .861 ±.022 .823 ±.022 .879 ±.019 .858 ±.018 .889 ±.032 4 .886 ±.030 .900 ±.028 .882 ±.032 .869 ±.011 .850 ±.011 .898 ±.024 .895 ±.024 .867 ±.008 .887 ±.015 .895 ±.024 .884 ±.032 .870 ±.046 .891 ±.026 .881 ±.014 .895 ±.028 5 .898 ±.024 .901 ±.025 .898 ±.024 .883 ±.022 .886 ±.032 .898 ±.019 .896 ±.033 .898 ±.010 .903 ±.022 .901 ±.021 .891 ±.021 .882 ±.041 .902 ±.027 .902 ±.021 .905 ±.021 polish_companies bankruptcy 1 .953 ±.011 .838 ±.004 .864 ±.014 .885 ±.018 .833 ±.006 .841 ±.001 .940 ±.008 .937 ±.015 .920 ±.016 .922 ±.020 .847 ±.007 .844 ±.014 .873 ±.022 .945 ±.019 .939 ±.015 2 .954 ±.015 .871 ±.030 .881 ±.022 .925 ±.030 .873 ±.020 .863 ±.033 .952 ±.013 .949 ±.013 .940 ±.017 .943 ±.016 .938 ±.034 .897 ±.048 .926 ±.036 .953 ±.018 .949 ±.016 3 .961 ±.013 .908 ±.042 .878 ±.021 .951 ±.017 .874 ±.017 .903 ±.046 .956 ±.020 .952 ±.017 .948 ±.018 .954 ±.015 .943 ±.030 .901 ±.055 .926 ±.019 .956 ±.014 .948 ±.012 4 .961 ±.016 .912 ±.028 .917 ±.040 .952 ±.014 .882 ±.015 .911 ±.029 .954 ±.014 .954 ±.015 .951 ±.018 .960 ±.012 .954 ±.018 .951 ±.012 .949 ±.035 .962 ±.009 .953 ±.014 5 .958 ±.013 .946 ±.021 .951 ±.012 .952 ±.015 .903 ±.003 .947 ±.020 .957 ±.015 .959 ±.011 .955 ±.014 .959 ±.013 .960 ±.012 .951 ±.008 .963 ±.010 .960 ±.013 .959 ±.011 colon_tumor 1 .710 ±.022 .844 ±.070 .823 ±.048 .837 ±.079 .575 ±.244 .797 ±.107 .655 ±.186 .846 ±.106 .863 ±.027 .816 ±.002 .877 ±.064 .612 ±.299 .658 ±.157 .763 ±.144 .646 ±.212 2 .852 ±.077 .853 ±.101 .903 ±.037 .899 ±.041 .856 ±.156 .834 ±.110 .881 ±.060 .788 ±.165 .871 ±.083 .841 ±.052 .917 ±.039 .846 ±.092 .819 ±.081 .875 ±.096 .782 ±.030 3 .837 ±.080 .880 ±.080 .868 ±.072 .883 ±.113 .892 ±.081 .859 ±.074 .787 ±.046 .888 ±.097 .869 ±.078 .857 ±.079 .887 ±.070 .805 ±.125 .859 ±.035 .848 ±.069 .872 ±.035 4 .870 ±.066 .842 ±.085 .892 ±.049 .875 ±.075 .861 ±.127 .870 ±.068 .883 ±.060 .875 ±.054 .915 ±.068 .862 ±.069 .888 ±.053 .870 ±.118 .826 ±.092 .898 ±.062 .873 ±.082 5 .913 ±.038 .872 ±.054 .879 ±.080 .885 ±.065 .892 ±.081 .899 ±.060 .889 ±.051 .879 ±.054 .906 ±.066 .889 ±.081 .910 ±.085 .904 ±.068 .911 ±.065 .882 ±.077 .907 ±.054 mechanisms of_action 1 .936 ±.011 .937 ±.007 .933 ±.007 .940 ±.010 .931 ±.009 .946 ±.013 .922 ±.015 .936 ±.009 .937 ±.010 .941 ±.005 .896 ±.052 .922 ±.013 .934 ±.006 .933 ±.006 .936 ±.009 2 .940 ±.005 .943 ±.008 .938 ±.009 .940 ±.009 .944 ±.007 .946 ±.006 .935 ±.010 .941 ±.007 .936 ±.009 .940 ±.011 .949 ±.005 .933 ±.003 .940 ±.004 .939 ±.010 .938 ±.011 3 .946 ±.010 .949 ±.011 .943 ±.013 .942 ±.008 .942 ±.006 .942 ±.010 .944 ±.006 .946 ±.006 .946 ±.010 .942 ±.005 .949 ±.006 .943 ±.009 .942 ±.004 .943 ±.009 .942 ±.012 4 .950 ±.008 .951 ±.007 .949 ±.005 .948 ±.005 .948 ±.008 .951 ±.007 .945 ±.009 .955 ±.009 .951 ±.014 .950 ±.009 .953 ±.011 .952 ±.008 .951 ±.007 .949 ±.011 .951 ±.009 5 .953 ±.013 .960 ±.006 .953 ±.011 .954 ±.015 .960 ±.004 .959 ±.009 .957 ±.008 .955 ±.006 .954 ±.010 .958 ±.008 .959 ±.008 .954 ±.004 .954 ±.008 .960 ±.010 .952 ±.009 santander_customer transaction_prediction 1 .630 ±.007 .631 ±.006 .632 ±.004 .632 ±.004 .499 ±.001 .630 ±.006 .594 ±.017 .632 ±.004 .502 ±.002 .632 ±.004 .501 ±.004 .515 ±.007 .534 ±.005 .632 ±.004 .574 ±.013 2 .698 ±.003 .701 ±.002 .704 ±.002 .702 ±.003 .501 ±.001 .701 ±.002 .619 ±.020 .702 ±.004 .510 ±.002 .702 ±.004 .509 ±.003 .575 ±.027 .579 ±.031 .700 ±.000 .601 ±.020 3 .783 ±.004 .785 ±.004 .786 ±.005 .781 ±.004 .516 ±.001 .785 ±.004 .624 ±.010 .783 ±.003 .523 ±.004 .787 ±.003 .511 ±.003 .603 ±.033 .611 ±.020 .786 ±.002 .609 ±.013 4 .855 ±.004 .863 ±.003 .863 ±.003 .858 ±.005 .525 ±.001 .863 ±.003 .652 ±.015 .854 ±.003 .549 ±.004 .856 ±.004 .535 ±.007 .650 ±.014 .636 ±.018 .861 ±.003 .660 ±.017 5 .889 ±.003 .888 ±.002 .889 ±.003 .888 ±.002 .548 ±.006 .888 ±.002 .754 ±.014 .881 ±.003 .683 ±.004 .878 ±.002 .570 ±.025 .764 ±.022 .757 ±.008 .889 ±.002 .781 ±.017 nci_prostate cancer 1 .954 ±.032 .935 ±.016 .935 ±.011 .931 ±.007 .664 ±.045 .923 ±.047 .776 ±.071 .896 ±.062 .925 ±.010 .958 ±.010 .888 ±.015 .777 ±.025 .705 ±.089 .950 ±.030 .827 ±.078 2 .976 ±.006 .955 ±.039 .966 ±.016 .974 ±.020 .766 ±.122 .981 ±.011 .884 ±.033 .984 ±.005 .972 ±.002 .961 ±.020 .974 ±.023 .877 ±.034 .885 ±.038 .962 ±.026 .897 ±.010 3 .977 ±.008 .971 ±.009 .972 ±.012 .975 ±.005 .843 ±.012 .988 ±.004 .924 ±.007 .973 ±.004 .987 ±.002 .944 ±.042 .980 ±.006 .969 ±.017 .939 ±.014 .993 ±.002 .975 ±.007 4 .985 ±.010 .990 ±.003 .969 ±.022 .980 ±.010 .937 ±.021 .990 ±.007 .948 ±.037 .985 ±.002 .980 ±.019 .976 ±.013 .988 ±.007 .960 ±.040 .979 ±.008 .984 ±.006 .976 ±.011 5 .990 ±.007 .990 ±.008 .977 ±.030 .986 ±.018 .959 ±.027 .992 ±.006 .987 ±.009 .989 ±.008 .986 ±.017 .989 ±.005 .991 ±.001 .983 ±.010 .983 ±.012 .992 ±.005 .985 ±.005 bank_customer churn 1 .750 ±.010 .774 ±.014 .774 ±.014 .821 ±.004 .597 ±.011 .774 ±.014 .821 ±.004 .821 ±.004 .821 ±.003 .821 ±.004 .745 ±.065 .549 ±.061 .821 ±.003 .751 ±.009 .821 ±.004 2 .765 ±.007 .791 ±.007 .791 ±.007 .854 ±.008 .767 ±.004 .791 ±.007 .852 ±.009 .853 ±.009 .840 ±.011 .838 ±.008 .821 ±.004 .687 ±.070 .838 ±.010 .764 ±.008 .845 ±.004 3 .834 ±.002 .803 ±.007 .839 ±.024 .851 ±.012 .785 ±.007 .803 ±.007 .860 ±.015 .852 ±.010 .860 ±.011 .839 ±.007 .833 ±.003 .714 ±.091 .845 ±.002 .834 ±.002 .851 ±.004 4 .847 ±.000 .846 ±.033 .869 ±.008 .853 ±.010 .817 ±.010 .846 ±.033 .867 ±.004 .852 ±.009 .864 ±.012 .853 ±.003 .846 ±.006 .807 ±.046 .868 ±.007 .845 ±.004 .863 ±.012 5 .866 ±.007 .844 ±.034 .865 ±.007 .855 ±.012 .815 ±.011 .844 ±.034 .866 ±.008 .861 ±.005 .865 ±.007 .853 ±.004 .855 ±.010 .842 ±.034 .867 ±.008 .865 ±.008 .867 ±.008 hepatitis_survival prediction 1 .814 ±.029 .750 ±.116 .796 ±.024 .816 ±.032 .758 ±.113 .803 ±.054 .790 ±.069 .842 ±.010 .757 ±.026 .828 ±.043 .785 ±.040 .834 ±.021 .787 ±.044 .804 ±.046 .819 ±.005 2 .835 ±.047 .856 ±.038 .821 ±.021 .794 ±.063 .776 ±.073 .840 ±.051 .847 ±.055 .818 ±.043 .804 ±.019 .781 ±.044 .831 ±.045 .767 ±.068 .813 ±.047 .805 ±.039 .798 ±.055 3 .853 ±.016 .842 ±.040 .809 ±.023 .819 ±.034 .787 ±.057 .845 ±.019 .860 ±.039 .807 ±.023 .837 ±.074 .811 ±.037 .827 ±.005 .831 ±.007 .847 ±.033 .828 ±.037 .768 ±.046 4 .847 ±.036 .861 ±.025 .830 ±.020 .846 ±.035 .812 ±.050 .844 ±.019 .871 ±.041 .839 ±.030 .833 ±.034 .837 ±.022 .791 ±.073 .832 ±.045 .867 ±.016 .835 ±.034 .848 ±.012 5 .906 ±.013 .862 ±.047 .848 ±.051 .865 ±.026 .828 ±.019 .868 ±.048 .886 ±.020 .836 ±.000 .886 ±.050 .835 ±.010 .829 ±.051 .893 ±.035 .860 ±.004 .845 ±.025 .817 ±.038 customer_satisfaction in_airline 1 .957 ±.000 .920 ±.001 .935 ±.002 .924 ±.000 .940 ±.001 .920 ±.001 .941 ±.002 .961 ±.000 .959 ±.000 .913 ±.000 .956 ±.000 .858 ±.052 .949 ±.038 .959 ±.000 .972 ±.001 2 .979 ±.000 .977 ±.000 .957 ±.001 .976 ±.000 .979 ±.000 .977 ±.000 .977 ±.000 .978 ±.000 .978 ±.000 .973 ±.000 .977 ±.005 .890 ±.020 .985 ±.001 .976 ±.001 .985 ±.001 3 .990 ±.000 .989 ±.000 .982 ±.008 .987 ±.000 .989 ±.001 .989 ±.000 .991 ±.001 .988 ±.000 .988 ±.000 .985 ±.000 .989 ±.000 .979 ±.010 .991 ±.001 .987 ±.001 .992 ±.001 4 .993 ±.000 .991 ±.000 .992 ±.001 .991 ±.000 .993 ±.000 .991 ±.000 .994 ±.000 .991 ±.000 .992 ±.000 .991 ±.000 .993 ±.000 .991 ±.002 .994 ±.000 .992 ±.000 .993 ±.000 5 .994 ±.000 .994 ±.000 .994 ±.000 .993 ±.000 .994 ±.000 .994 ±.000 .995 ±.000 .994 ±.000 .994 ±.000 .994 ±.000 .994 ±.000 .994 ±.001 .995 ±.000 .995 ±.000 .994 ±.000 heloc 1 .778 ±.013 .770 ±.008 .775 ±.010 .771 ±.012 .667 ±.005 .773 ±.005 .761 ±.051 .787 ±.009 .773 ±.011 .777 ±.017 .733 ±.011 .725 ±.052 .785 ±.010 .776 ±.011 .768 ±.007 2 .780 ±.011 .782 ±.006 .787 ±.012 .786 ±.010 .770 ±.012 .785 ±.006 .785 ±.020 .791 ±.010 .789 ±.010 .792 ±.011 .792 ±.008 .755 ±.015 .791 ±.010 .784 ±.010 .791 ±.010 3 .794 ±.010 .790 ±.007 .795 ±.008 .795 ±.007 .771 ±.013 .791 ±.006 .789 ±.019 .794 ±.007 .796 ±.009 .794 ±.010 .795 ±.008 .772 ±.010 .793 ±.010 .794 ±.009 .793 ±.010 4 .796 ±.009 .798 ±.009 .796 ±.009 .796 ±.008 .781 ±.008 .797 ±.009 .795 ±.012 .797 ±.008 .797 ±.008 .797 ±.009 .797 ±.008 .787 ±.007 .797 ±.009 .796 ±.008 .794 ±.009 5 .797 ±.009 .798 ±.010 .797 ±.009 .798 ±.009 .794 ±.009 .798 ±.009 .797 ±.010 .799 ±.009 .798 ±.009 .797 ±.010 .798 ±.010 .794 ±.007 .797 ±.010 .798 ±.009 .797 ±.010 hiva agnostic 1 .599 ±.048 .654 ±.022 .548 ±.040 .550 ±.046 .491 ±.038 .588 ±.044 .548 ±.077 .515 ±.043 .596 ±.063 .550 ±.046 .607 ±.055 .563 ±.017 .570 ±.035 .594 ±.063 .656 ±.072 2 .669 ±.033 .709 ±.049 .627 ±.085 .623 ±.032 .534 ±.061 .743 ±.011 .625 ±.055 .611 ±.063 .625 ±.100 .623 ±.032 .666 ±.075 .623 ±.064 .605 ±.028 .696 ±.016 .676 ±.008 3 .710 ±.014 .738 ±.040 .664 ±.087 .712 ±.031 .619 ±.058 .742 ±.034 .661 ±.068 .723 ±.056 .708 ±.078 .712 ±.031 .741 ±.017 .690 ±.070 .692 ±.064 .767 ±.017 .731 ±.062 4 .744 ±.004 .765 ±.061 .779 ±.020 .740 ±.042 .681 ±.029 .738 ±.021 .708 ±.063 .741 ±.042 .745 ±.054 .740 ±.042 .766 ±.008 .761 ±.033 .731 ±.064 .788 ±.018 .762 ±.007 5 .744 ±.054 .772 ±.042 .789 ±.009 .760 ±.020 .767 ±.012 .754 ±.009 .751 ±.038 .758 ±.049 .741 ±.060 .760 ±.020 .774 ±.029 .739 ±.031 .747 ±.039 .794 ±.010 .775 ±.039 68 (continued from previous page) DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCOMarkovBlanketMImRMR(R)ReliefFRandomSBERFImportanceSFS bank marketing 1 .672 ±.017 .711 ±.010 .711 ±.010 .679 ±.010 .624 ±.007 .711 ±.010 .701 ±.010 .699 ±.007 .674 ±.011 .670 ±.007 .677 ±.008 .675 ±.021 .710 ±.006 .662 ±.005 .714 ±.008 2 .716 ±.010 .729 ±.010 .728 ±.008 .721 ±.011 .651 ±.007 .729 ±.010 .734 ±.013 .729 ±.007 .732 ±.012 .697 ±.009 .702 ±.009 .706 ±.028 .731 ±.004 .713 ±.014 .734 ±.010 3 .724 ±.012 .740 ±.010 .742 ±.010 .734 ±.009 .736 ±.010 .740 ±.010 .746 ±.016 .735 ±.011 .749 ±.007 .732 ±.006 .717 ±.009 .735 ±.016 .748 ±.016 .723 ±.011 .738 ±.007 4 .744 ±.018 .757 ±.010 .752 ±.012 .742 ±.011 .745 ±.012 .755 ±.013 .758 ±.014 .743 ±.010 .756 ±.010 .754 ±.011 .746 ±.010 .746 ±.007 .754 ±.014 .730 ±.011 .749 ±.013 5 .759 ±.011 .760 ±.015 .757 ±.011 .747 ±.007 .757 ±.011 .761 ±.013 .759 ±.012 .757 ±.015 .758 ±.011 .759 ±.011 .757 ±.012 .752 ±.015 .758 ±.011 .759 ±.010 .754 ±.013 leukemia allaml 1 .910 ±.029 .981 ±.022 .983 ±.023 .967 ±.035 .606 ±.164 .971 ±.024 .711 ±.174 .988 ±.021 .980 ±.018 .975 ±.023 .978 ±.026 .798 ±.149 .726 ±.144 .966 ±.035 .617 ±.036 2 .926 ±.060 .968 ±.028 .954 ±.048 .983 ±.023 .900 ±.106 .982 ±.011 .797 ±.133 .903 ±.148 .995 ±.009 .966 ±.030 .972 ±.025 .754 ±.083 .865 ±.077 .995 ±.009 .848 ±.125 3 .958 ±.054 .987 ±.012 .968 ±.043 .988 ±.015 .946 ±.023 .990 ±.017 .952 ±.049 .978 ±.038 .965 ±.040 .985 ±.026 .980 ±.023 .932 ±.044 .921 ±.037 .980 ±.034 .969 ±.036 4 .997 ±.005 .990 ±.017 .990 ±.017 .995 ±.009 .969 ±.028 .978 ±.038 .985 ±.020 .995 ±.009 .993 ±.013 .995 ±.009 .998 ±.004 .978 ±.027 .978 ±.027 .988 ±.021 .998 ±.004 5 .992 ±.007 .998 ±.004 .993 ±.013 .988 ±.021 .993 ±.013 .985 ±.026 .998 ±.004 .995 ±.009 .990 ±.017 .998 ±.004 .995 ±.009 .990 ±.011 .995 ±.009 .990 ±.017 .985 ±.020 heart_disease cleveland 1 .832 ±.025 .845 ±.016 .818 ±.002 .841 ±.068 .778 ±.020 .848 ±.042 .847 ±.081 .801 ±.026 .842 ±.040 .846 ±.025 .850 ±.035 .828 ±.025 .836 ±.049 .812 ±.057 .829 ±.031 2 .861 ±.041 .867 ±.016 .869 ±.022 .897 ±.028 .813 ±.015 .853 ±.047 .856 ±.065 .847 ±.038 .880 ±.021 .863 ±.036 .883 ±.012 .842 ±.013 .824 ±.057 .893 ±.045 .847 ±.032 3 .868 ±.040 .900 ±.017 .889 ±.022 .908 ±.031 .859 ±.007 .875 ±.034 .864 ±.049 .876 ±.027 .890 ±.038 .876 ±.026 .897 ±.026 .818 ±.046 .868 ±.039 .881 ±.042 .879 ±.060 4 .881 ±.020 .904 ±.016 .897 ±.024 .896 ±.033 .872 ±.042 .895 ±.021 .868 ±.052 .903 ±.037 .893 ±.048 .901 ±.018 .892 ±.024 .901 ±.029 .867 ±.060 .890 ±.032 .898 ±.036 5 .899 ±.025 .891 ±.030 .905 ±.032 .900 ±.029 .892 ±.035 .894 ±.051 .892 ±.044 .901 ±.046 .901 ±.045 .896 ±.046 .901 ±.006 .906 ±.034 .892 ±.051 .900 ±.037 .902 ±.034 clock_protein period 1 .556 ±.092 .617 ±.189 .584 ±.066 .476 ±.073 .616 ±.041 .617 ±.189 .529 ±.154 .575 ±.035 .455 ±.035 .461 ±.133 .576 ±.116 .661 ±.193 .619 ±.061 .589 ±.122 .638 ±.027 2 .608 ±.115 .651 ±.139 .619 ±.125 .591 ±.228 .617 ±.067 .651 ±.139 .605 ±.042 .593 ±.060 .506 ±.058 .549 ±.166 .571 ±.127 .584 ±.058 .600 ±.076 .563 ±.115 .621 ±.106 3 .598 ±.161 .624 ±.141 .626 ±.162 .582 ±.125 .617 ±.077 .624 ±.141 .608 ±.153 .575 ±.095 .624 ±.120 .559 ±.144 .487 ±.115 .578 ±.336 .591 ±.095 .578 ±.176 .605 ±.119 4 .642 ±.148 .591 ±.130 .601 ±.185 .600 ±.170 .614 ±.061 .591 ±.130 .612 ±.132 .603 ±.125 .623 ±.095 .557 ±.160 .577 ±.188 .587 ±.173 .526 ±.169 .580 ±.090 .584 ±.070 5 .578 ±.153 .589 ±.164 .670 ±.197 .570 ±.186 .684 ±.119 .589 ±.164 .635 ±.179 .616 ±.165 .598 ±.115 .534 ±.147 .617 ±.165 .653 ±.178 .675 ±.139 .582 ±.153 .594 ±.176 churn 1 .827 ±.018 .885 ±.009 .877 ±.010 .815 ±.003 .715 ±.001 .786 ±.186 .711 ±.005 .813 ±.007 .876 ±.011 .865 ±.038 .800 ±.007 .689 ±.113 .891 ±.006 .876 ±.010 .893 ±.003 2 .900 ±.033 .907 ±.002 .909 ±.003 .843 ±.017 .755 ±.070 .860 ±.090 .821 ±.089 .853 ±.001 .889 ±.005 .897 ±.012 .817 ±.010 .716 ±.112 .915 ±.011 .900 ±.001 .914 ±.009 3 .924 ±.006 .919 ±.002 .912 ±.010 .871 ±.020 .849 ±.013 .921 ±.006 .843 ±.066 .848 ±.013 .899 ±.008 .919 ±.002 .903 ±.003 .756 ±.119 .922 ±.008 .909 ±.008 .915 ±.014 4 .922 ±.007 .921 ±.005 .919 ±.009 .864 ±.020 .841 ±.021 .927 ±.006 .880 ±.067 .845 ±.011 .911 ±.013 .921 ±.005 .911 ±.009 .866 ±.020 .919 ±.012 .922 ±.007 .922 ±.011 5 .921 ±.006 .922 ±.006 .923 ±.005 .862 ±.016 .916 ±.002 .924 ±.007 .922 ±.011 .844 ±.012 .916 ±.006 .920 ±.010 .912 ±.010 .889 ±.055 .920 ±.011 .925 ±.006 .923 ±.007 aps_failure 1 .977 ±.007 .949 ±.050 .976 ±.005 .962 ±.013 .926 ±.008 .973 ±.005 .937 ±.029 .977 ±.005 .979 ±.005 .973 ±.005 .940 ±.002 .964 ±.015 .964 ±.006 .981 ±.006 .978 ±.003 2 .984 ±.004 .977 ±.012 .977 ±.005 .976 ±.003 .957 ±.005 .979 ±.001 .971 ±.009 .981 ±.004 .981 ±.003 .982 ±.005 .962 ±.002 .975 ±.011 .973 ±.013 .984 ±.004 .980 ±.005 3 .988 ±.004 .987 ±.002 .982 ±.005 .973 ±.010 .977 ±.003 .986 ±.002 .985 ±.003 .987 ±.003 .983 ±.004 .985 ±.004 .982 ±.004 .983 ±.005 .985 ±.004 .987 ±.005 .984 ±.004 4 .990 ±.003 .990 ±.002 .986 ±.005 .988 ±.004 .987 ±.003 .989 ±.002 .988 ±.003 .990 ±.003 .983 ±.004 .987 ±.003 .986 ±.004 .990 ±.002 .988 ±.002 .989 ±.004 .988 ±.004 5 .991 ±.003 .991 ±.003 .990 ±.004 .990 ±.004 .989 ±.004 .991 ±.003 .990 ±.003 .991 ±.003 .989 ±.005 .990 ±.003 .990 ±.003 .991 ±.003 .990 ±.004 .990 ±.004 .990 ±.004 labor negotiations 1 .837 ±.060 .805 ±.160 .896 ±.039 .850 ±.157 .915 ±.057 .758 ±.149 .823 ±.143 .848 ±.136 .866 ±.080 .867 ±.172 .959 ±.018 .760 ±.151 .812 ±.287 .866 ±.080 .793 ±.175 2 .917 ±.059 .955 ±.040 .930 ±.061 .937 ±.081 .954 ±.052 .915 ±.117 .940 ±.074 .932 ±.062 .942 ±.053 .931 ±.082 .976 ±.041 .817 ±.257 .909 ±.115 .963 ±.032 .891 ±.118 3 .940 ±.062 .950 ±.051 .972 ±.038 .972 ±.030 .972 ±.038 .953 ±.071 .972 ±.048 .969 ±.030 .952 ±.056 .934 ±.060 .980 ±.018 .944 ±.096 .968 ±.036 .976 ±.041 .930 ±.079 4 .959 ±.037 .984 ±.018 .976 ±.041 .984 ±.027 .984 ±.018 .980 ±.018 .984 ±.027 .976 ±.041 .987 ±.013 .971 ±.026 .956 ±.049 .976 ±.041 .992 ±.014 .988 ±.012 .975 ±.026 5 .975 ±.021 .996 ±.007 .992 ±.014 .954 ±.052 .988 ±.012 .979 ±.027 .976 ±.024 .980 ±.018 .975 ±.021 .992 ±.007 .964 ±.052 .958 ±.045 .996 ±.007 .984 ±.014 .992 ±.007 kdd_cup_09 appetency 1 .673 ±.083 .525 ±.016 .781 ±.024 .757 ±.020 .542 ±.041 .525 ±.016 .812 ±.017 .784 ±.020 .629 ±.007 .759 ±.021 .653 ±.020 .535 ±.023 .522 ±.021 .755 ±.022 .792 ±.053 2 .740 ±.062 .554 ±.044 .787 ±.026 .756 ±.020 .569 ±.057 .554 ±.044 .815 ±.015 .809 ±.017 .636 ±.007 .779 ±.024 .655 ±.004 .548 ±.009 .539 ±.023 .782 ±.022 .811 ±.015 3 .803 ±.031 .576 ±.023 .793 ±.018 .803 ±.017 .571 ±.045 .576 ±.023 .813 ±.014 .807 ±.022 .729 ±.092 .790 ±.024 .801 ±.017 .606 ±.041 .616 ±.017 .796 ±.028 .814 ±.018 4 .820 ±.018 .613 ±.054 .809 ±.019 .810 ±.021 .615 ±.008 .613 ±.054 .817 ±.015 .808 ±.016 .785 ±.026 .816 ±.023 .815 ±.016 .714 ±.066 .694 ±.055 .795 ±.016 .799 ±.043 5 .817 ±.015 .715 ±.106 .816 ±.020 .812 ±.016 .729 ±.013 .715 ±.106 .813 ±.014 .804 ±.021 .789 ±.025 .817 ±.020 .819 ±.019 .745 ±.048 .746 ±.056 .816 ±.021 .810 ±.028 qsar_oral toxicity 1 .719 ±.014 .691 ±.041 .693 ±.025 .564 ±.062 .559 ±.018 .696 ±.035 .565 ±.033 .556 ±.044 .686 ±.022 .564 ±.062 .562 ±.036 .580 ±.014 .542 ±.059 .690 ±.025 .739 ±.011 2 .775 ±.011 .749 ±.027 .717 ±.033 .624 ±.028 .584 ±.006 .782 ±.017 .619 ±.011 .633 ±.044 .728 ±.025 .624 ±.028 .680 ±.017 .633 ±.048 .662 ±.045 .736 ±.042 .763 ±.008 3 .833 ±.018 .821 ±.025 .798 ±.028 .741 ±.028 .722 ±.017 .836 ±.018 .763 ±.022 .759 ±.035 .811 ±.017 .741 ±.028 .815 ±.018 .754 ±.022 .756 ±.007 .823 ±.037 .821 ±.036 4 .868 ±.017 .854 ±.027 .873 ±.024 .850 ±.021 .840 ±.019 .861 ±.030 .848 ±.024 .851 ±.027 .865 ±.018 .850 ±.021 .882 ±.025 .839 ±.029 .843 ±.017 .881 ±.018 .851 ±.040 5 .886 ±.028 .875 ±.034 .882 ±.025 .876 ±.021 .871 ±.030 .877 ±.023 .877 ±.019 .879 ±.025 .892 ±.017 .876 ±.021 .890 ±.023 .876 ±.023 .872 ±.019 .886 ±.030 .879 ±.023 taiwanese_bankruptcy prediction 1 .933 ±.011 .937 ±.006 .934 ±.004 .943 ±.007 .940 ±.003 .933 ±.015 .925 ±.008 .938 ±.004 .941 ±.005 .937 ±.009 .924 ±.010 .935 ±.008 .938 ±.009 .942 ±.007 .940 ±.010 2 .939 ±.007 .944 ±.007 .938 ±.005 .940 ±.005 .941 ±.004 .942 ±.004 .944 ±.008 .939 ±.003 .941 ±.004 .940 ±.001 .938 ±.006 .940 ±.011 .939 ±.001 .942 ±.005 .943 ±.008 3 .943 ±.004 .946 ±.008 .937 ±.007 .944 ±.005 .944 ±.008 .944 ±.008 .942 ±.008 .941 ±.006 .940 ±.004 .945 ±.004 .939 ±.003 .942 ±.006 .941 ±.005 .941 ±.003 .945 ±.010 4 .943 ±.005 .946 ±.007 .941 ±.001 .941 ±.004 .942 ±.003 .947 ±.007 .945 ±.002 .940 ±.007 .943 ±.006 .941 ±.005 .944 ±.002 .942 ±.005 .944 ±.006 .942 ±.004 .944 ±.006 5 .944 ±.006 .942 ±.003 .941 ±.001 .943 ±.001 .944 ±.003 .942 ±.004 .943 ±.004 .941 ±.003 .945 ±.007 .942 ±.002 .940 ±.004 .945 ±.005 .941 ±.005 .946 ±.004 .943 ±.004 thyroid discordant 1 .974 ±.013 .785 ±.224 .909 ±.041 .978 ±.004 .534 ±.003 .785 ±.224 .967 ±.013 .968 ±.013 .914 ±.030 .935 ±.013 .935 ±.033 .719 ±.213 .970 ±.016 .981 ±.005 .929 ±.060 2 .980 ±.008 .939 ±.045 .933 ±.047 .978 ±.005 .572 ±.002 .939 ±.045 .973 ±.014 .980 ±.005 .924 ±.022 .943 ±.019 .937 ±.024 .784 ±.173 .983 ±.005 .980 ±.008 .929 ±.071 3 .973 ±.009 .942 ±.041 .942 ±.070 .980 ±.007 .583 ±.016 .942 ±.041 .981 ±.008 .979 ±.006 .936 ±.024 .928 ±.065 .933 ±.067 .971 ±.019 .977 ±.012 .961 ±.040 .924 ±.087 4 .966 ±.018 .959 ±.024 .963 ±.037 .974 ±.021 .636 ±.031 .959 ±.024 .982 ±.006 .969 ±.022 .948 ±.039 .944 ±.062 .977 ±.015 .977 ±.014 .978 ±.006 .966 ±.034 .926 ±.084 5 .964 ±.034 .959 ±.024 .979 ±.008 .968 ±.027 .927 ±.066 .959 ±.024 .976 ±.005 .966 ±.015 .935 ±.052 .952 ±.035 .977 ±.024 .981 ±.007 .984 ±.006 .960 ±.036 .955 ±.041 coil_2000 insurance_policies 1 .732 ±.027 .708 ±.056 .732 ±.026 .738 ±.023 .528 ±.015 .661 ±.092 .722 ±.033 .727 ±.034 .731 ±.013 .719 ±.036 .650 ±.022 .708 ±.032 .657 ±.008 .724 ±.023 .742 ±.026 2 .727 ±.023 .728 ±.033 .737 ±.029 .732 ±.030 .567 ±.035 .719 ±.040 .727 ±.025 .729 ±.024 .737 ±.031 .726 ±.041 .653 ±.018 .732 ±.032 .704 ±.057 .732 ±.030 .742 ±.031 3 .740 ±.040 .732 ±.032 .739 ±.039 .736 ±.034 .640 ±.043 .721 ±.036 .726 ±.032 .731 ±.021 .737 ±.031 .725 ±.039 .700 ±.020 .706 ±.012 .705 ±.060 .732 ±.017 .735 ±.026 4 .739 ±.035 .729 ±.027 .741 ±.029 .732 ±.034 .657 ±.030 .728 ±.034 .731 ±.035 .736 ±.020 .740 ±.026 .732 ±.024 .723 ±.028 .745 ±.028 .731 ±.030 .736 ±.035 .743 ±.036 5 .742 ±.027 .740 ±.026 .737 ±.031 .734 ±.034 .655 ±.030 .731 ±.020 .734 ±.026 .738 ±.037 .739 ±.030 .731 ±.019 .724 ±.034 .735 ±.030 .737 ±.029 .738 ±.026 .738 ±.029 prostate cancer 1 .923 ±.088 .957 ±.050 .938 ±.048 .948 ±.070 .617 ±.077 .943 ±.035 .689 ±.108 .947 ±.044 .938 ±.054 .942 ±.086 .946 ±.052 .634 ±.112 .628 ±.105 .949 ±.041 .851 ±.103 2 .897 ±.089 .953 ±.065 .949 ±.071 .956 ±.062 .710 ±.081 .967 ±.052 .713 ±.155 .947 ±.077 .943 ±.077 .957 ±.072 .973 ±.041 .728 ±.055 .686 ±.161 .967 ±.043 .913 ±.035 3 .947 ±.041 .954 ±.058 .957 ±.074 .953 ±.069 .819 ±.040 .969 ±.045 .838 ±.096 .967 ±.052 .956 ±.076 .955 ±.075 .963 ±.052 .849 ±.026 .898 ±.059 .965 ±.033 .859 ±.064 4 .956 ±.038 .971 ±.036 .963 ±.045 .969 ±.045 .860 ±.075 .964 ±.053 .955 ±.043 .962 ±.042 .960 ±.058 .971 ±.044 .968 ±.050 .937 ±.030 .924 ±.045 .963 ±.040 .938 ±.056 5 .938 ±.057 .963 ±.052 .969 ±.043 .961 ±.051 .947 ±.042 .957 ±.054 .960 ±.032 .958 ±.066 .960 ±.056 .962 ±.044 .975 ±.035 .958 ±.052 .946 ±.069 .953 ±.055 .953 ±.050 credit approval 1 .903 ±.022 .913 ±.023 .913 ±.006 .812 ±.018 .665 ±.081 .912 ±.006 .897 ±.021 .835 ±.001 .915 ±.009 .824 ±.014 .874 ±.032 .771 ±.072 .912 ±.017 .911 ±.011 .910 ±.018 2 .917 ±.019 .907 ±.018 .919 ±.011 .840 ±.004 .761 ±.038 .912 ±.020 .910 ±.001 .841 ±.006 .911 ±.006 .851 ±.053 .885 ±.006 .840 ±.112 .916 ±.023 .910 ±.016 .914 ±.016 3 .902 ±.022 .913 ±.017 .917 ±.020 .850 ±.015 .898 ±.011 .917 ±.004 .910 ±.014 .850 ±.003 .912 ±.006 .896 ±.041 .904 ±.017 .851 ±.041 .911 ±.021 .923 ±.022 .904 ±.019 4 .909 ±.022 .914 ±.013 .924 ±.011 .870 ±.024 .899 ±.004 .920 ±.012 .916 ±.014 .852 ±.006 .916 ±.013 .916 ±.009 .918 ±.013 .867 ±.045 .913 ±.018 .911 ±.015 .914 ±.007 5 .909 ±.012 .916 ±.015 .925 ±.011 .910 ±.011 .903 ±.015 .912 ±.014 .916 ±.007 .899 ±.035 .912 ±.013 .921 ±.004 .928 ±.013 .885 ±.037 .920 ±.010 .908 ±.017 .916 ±.006 69 (continued from previous page) DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCOMarkovBlanketMImRMR(R)ReliefFRandomSBERFImportanceSFS heart_disease hungary 1 .790 ±.026 .879 ±.020 .862 ±.020 .833 ±.078 .675 ±.142 .886 ±.018 .744 ±.160 .834 ±.048 .832 ±.057 .844 ±.055 .874 ±.037 .726 ±.145 .835 ±.038 .852 ±.024 .844 ±.033 2 .821 ±.033 .901 ±.034 .892 ±.025 .883 ±.020 .881 ±.042 .889 ±.014 .726 ±.204 .865 ±.043 .860 ±.044 .857 ±.035 .873 ±.047 .846 ±.016 .847 ±.071 .841 ±.026 .863 ±.050 3 .837 ±.025 .894 ±.030 .892 ±.029 .867 ±.046 .840 ±.038 .887 ±.027 .852 ±.060 .858 ±.032 .879 ±.037 .868 ±.027 .888 ±.026 .853 ±.035 .885 ±.024 .877 ±.023 .891 ±.055 4 .864 ±.025 .897 ±.013 .889 ±.024 .847 ±.043 .890 ±.037 .873 ±.049 .875 ±.043 .864 ±.055 .891 ±.047 .902 ±.047 .873 ±.027 .868 ±.010 .903 ±.025 .870 ±.015 .893 ±.029 5 .886 ±.031 .896 ±.035 .882 ±.025 .877 ±.016 .895 ±.015 .876 ±.038 .872 ±.056 .883 ±.058 .893 ±.040 .894 ±.027 .883 ±.011 .869 ±.036 .888 ±.022 .875 ±.027 .890 ±.014 give_me some_credit 1 .617 ±.001 .732 ±.001 .732 ±.005 .733 ±.006 .609 ±.021 .732 ±.001 .759 ±.003 .733 ±.006 .759 ±.002 .733 ±.005 .659 ±.009 .702 ±.035 .751 ±.011 .616 ±.001 .759 ±.002 2 .770 ±.002 .808 ±.013 .812 ±.003 .784 ±.003 .705 ±.013 .808 ±.013 .811 ±.013 .785 ±.002 .796 ±.004 .809 ±.008 .687 ±.003 .793 ±.019 .796 ±.024 .699 ±.007 .799 ±.003 3 .778 ±.003 .824 ±.004 .826 ±.003 .786 ±.002 .740 ±.040 .824 ±.004 .824 ±.009 .797 ±.004 .825 ±.003 .826 ±.003 .787 ±.002 .796 ±.037 .824 ±.006 .778 ±.004 .810 ±.002 4 .820 ±.014 .828 ±.003 .831 ±.002 .794 ±.004 .802 ±.025 .828 ±.003 .840 ±.004 .800 ±.006 .835 ±.003 .828 ±.003 .817 ±.020 .820 ±.013 .842 ±.005 .836 ±.011 .832 ±.003 5 .845 ±.004 .838 ±.003 .840 ±.003 .830 ±.008 .836 ±.012 .838 ±.003 .844 ±.004 .826 ±.002 .844 ±.003 .838 ±.003 .842 ±.003 .844 ±.003 .844 ±.004 .844 ±.004 .841 ±.008 clock_protein toxicity 1 .479 ±.066 .488 ±.099 .434 ±.105 .534 ±.066 .540 ±.090 .488 ±.099 .444 ±.047 .483 ±.067 .559 ±.118 .459 ±.068 .439 ±.099 .506 ±.067 .469 ±.063 .483 ±.096 .476 ±.055 2 .545 ±.090 .518 ±.074 .505 ±.046 .534 ±.108 .497 ±.062 .518 ±.074 .505 ±.064 .495 ±.071 .556 ±.092 .544 ±.126 .421 ±.112 .502 ±.043 .560 ±.021 .539 ±.104 .550 ±.097 3 .517 ±.103 .558 ±.075 .521 ±.034 .576 ±.120 .441 ±.032 .558 ±.075 .489 ±.057 .539 ±.044 .515 ±.165 .516 ±.104 .451 ±.108 .494 ±.118 .495 ±.043 .516 ±.122 .521 ±.133 4 .544 ±.092 .516 ±.099 .481 ±.042 .528 ±.074 .482 ±.059 .516 ±.099 .505 ±.036 .529 ±.077 .546 ±.090 .496 ±.105 .504 ±.085 .513 ±.080 .528 ±.102 .516 ±.085 .521 ±.139 5 .527 ±.073 .511 ±.069 .508 ±.053 .465 ±.058 .533 ±.055 .511 ±.069 .542 ±.109 .486 ±.059 .551 ±.085 .529 ±.104 .538 ±.101 .500 ±.090 .516 ±.089 .495 ±.067 .531 ±.098 online_shoppers_purchasing intention_dataset 1 .895 ±.005 .898 ±.008 .893 ±.005 .890 ±.009 .672 ±.010 .898 ±.008 .919 ±.000 .910 ±.014 .893 ±.012 .919 ±.003 .714 ±.037 .698 ±.018 .924 ±.005 .898 ±.008 .882 ±.009 2 .896 ±.007 .927 ±.005 .906 ±.009 .910 ±.024 .708 ±.011 .928 ±.005 .923 ±.001 .930 ±.004 .906 ±.019 .925 ±.004 .864 ±.104 .713 ±.027 .925 ±.005 .895 ±.009 .922 ±.005 3 .925 ±.004 .927 ±.002 .930 ±.005 .929 ±.005 .746 ±.002 .924 ±.002 .921 ±.003 .928 ±.005 .925 ±.002 .924 ±.005 .924 ±.007 .900 ±.014 .923 ±.004 .925 ±.005 .924 ±.003 4 .924 ±.003 .926 ±.000 .924 ±.004 .924 ±.005 .922 ±.005 .925 ±.001 .922 ±.005 .924 ±.003 .926 ±.004 .928 ±.005 .928 ±.007 .920 ±.009 .925 ±.004 .923 ±.003 .923 ±.002 5 .923 ±.004 .924 ±.002 .924 ±.006 .923 ±.002 .923 ±.005 .924 ±.001 .924 ±.004 .924 ±.003 .924 ±.003 .926 ±.005 .928 ±.006 .861 ±.083 .924 ±.003 .924 ±.003 .922 ±.003 credit_card clients_default 1 .757 ±.006 .758 ±.007 .742 ±.003 .742 ±.003 .681 ±.001 .757 ±.007 .762 ±.004 .764 ±.008 .743 ±.004 .751 ±.003 .752 ±.003 .696 ±.031 .754 ±.011 .762 ±.003 .750 ±.011 2 .772 ±.003 .772 ±.012 .777 ±.006 .779 ±.006 .729 ±.004 .768 ±.010 .773 ±.009 .777 ±.005 .770 ±.006 .764 ±.003 .773 ±.002 .743 ±.038 .776 ±.003 .769 ±.007 .769 ±.010 3 .776 ±.006 .776 ±.010 .780 ±.006 .780 ±.007 .756 ±.021 .776 ±.009 .781 ±.006 .783 ±.007 .781 ±.004 .778 ±.004 .782 ±.000 .770 ±.019 .780 ±.006 .774 ±.004 .777 ±.002 4 .779 ±.005 .781 ±.004 .783 ±.005 .783 ±.003 .775 ±.004 .781 ±.004 .783 ±.005 .783 ±.007 .783 ±.006 .781 ±.006 .783 ±.002 .772 ±.019 .783 ±.006 .780 ±.004 .780 ±.003 5 .782 ±.004 .785 ±.004 .786 ±.005 .785 ±.005 .785 ±.005 .784 ±.005 .785 ±.005 .784 ±.007 .785 ±.005 .786 ±.005 .785 ±.004 .774 ±.023 .785 ±.005 .785 ±.006 .783 ±.003 bioresponse 1 .804 ±.010 .765 ±.009 .777 ±.001 .668 ±.014 .498 ±.002 .770 ±.007 .630 ±.048 .766 ±.027 .793 ±.009 .735 ±.026 .759 ±.004 .535 ±.027 .537 ±.012 .795 ±.018 .764 ±.030 2 .826 ±.016 .804 ±.012 .818 ±.018 .735 ±.016 .506 ±.009 .813 ±.013 .716 ±.039 .819 ±.015 .836 ±.011 .771 ±.025 .800 ±.007 .723 ±.034 .667 ±.064 .836 ±.015 .783 ±.018 3 .846 ±.013 .841 ±.012 .834 ±.013 .816 ±.018 .516 ±.004 .830 ±.009 .803 ±.011 .848 ±.015 .849 ±.014 .801 ±.007 .834 ±.004 .761 ±.034 .743 ±.020 .850 ±.011 .815 ±.015 4 .861 ±.006 .846 ±.010 .851 ±.012 .848 ±.011 .519 ±.009 .844 ±.008 .821 ±.027 .857 ±.006 .856 ±.009 .845 ±.015 .841 ±.009 .819 ±.036 .817 ±.027 .859 ±.007 .832 ±.012 5 .863 ±.004 .848 ±.012 .859 ±.009 .857 ±.014 .765 ±.031 .848 ±.006 .850 ±.016 .863 ±.012 .858 ±.003 .854 ±.015 .853 ±.012 .850 ±.010 .845 ±.012 .862 ±.010 .852 ±.013 lymphography 1 .892 ±.055 .828 ±.135 .842 ±.059 .803 ±.068 .630 ±.062 .821 ±.131 .844 ±.129 .829 ±.050 .867 ±.035 .847 ±.041 .864 ±.073 .786 ±.114 .868 ±.079 .862 ±.045 .861 ±.085 2 .929 ±.029 .840 ±.096 .907 ±.013 .859 ±.046 .715 ±.127 .837 ±.090 .907 ±.060 .830 ±.014 .898 ±.032 .859 ±.053 .913 ±.019 .740 ±.224 .885 ±.036 .932 ±.034 .876 ±.056 3 .934 ±.014 .869 ±.098 .888 ±.057 .896 ±.031 .844 ±.132 .851 ±.086 .895 ±.082 .877 ±.025 .925 ±.037 .885 ±.015 .923 ±.038 .859 ±.032 .908 ±.034 .929 ±.023 .856 ±.083 4 .927 ±.020 .891 ±.078 .896 ±.031 .912 ±.023 .897 ±.061 .877 ±.055 .878 ±.047 .891 ±.057 .910 ±.024 .883 ±.040 .912 ±.050 .879 ±.065 .901 ±.051 .918 ±.039 .861 ±.109 5 .921 ±.043 .904 ±.055 .908 ±.040 .890 ±.017 .887 ±.083 .887 ±.062 .905 ±.069 .910 ±.053 .901 ±.028 .903 ±.084 .930 ±.040 .900 ±.063 .892 ±.084 .924 ±.045 .890 ±.104 nci_ovarian cancer 1 .985 ±.019 .986 ±.020 .988 ±.014 .991 ±.013 .699 ±.109 1.00 ±.00 .851 ±.047 .984 ±.027 .984 ±.013 .993 ±.010 .988 ±.014 .796 ±.191 .829 ±.093 .990 ±.017 .989 ±.012 2 .998 ±.003 1.00 ±.00 .999 ±.001 1.00 ±.00 .900 ±.104 1.00 ±.00 .979 ±.017 1.00 ±.00 .999 ±.002 1.00 ±.00 1.00 ±.00 .990 ±.014 .986 ±.022 1.00 ±.00 .998 ±.002 3 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 .945 ±.062 1.00 ±.00 .998 ±.003 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 .999 ±.001 .998 ±.003 1.00 ±.00 .999 ±.002 4 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 .993 ±.012 1.00 ±.00 .999 ±.001 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 5 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 .997 ±.004 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 bad_customer detection 1 .598 ±.025 .609 ±.024 .648 ±.034 .704 ±.040 .653 ±.042 .609 ±.024 .648 ±.058 .644 ±.059 .688 ±.029 .665 ±.018 .604 ±.043 .658 ±.024 .682 ±.016 .599 ±.013 .708 ±.018 2 .701 ±.039 .628 ±.017 .661 ±.020 .724 ±.047 .665 ±.040 .628 ±.017 .664 ±.049 .695 ±.039 .742 ±.032 .703 ±.021 .623 ±.008 .570 ±.033 .696 ±.031 .697 ±.043 .718 ±.010 3 .712 ±.042 .663 ±.010 .665 ±.028 .726 ±.042 .660 ±.046 .663 ±.010 .708 ±.044 .734 ±.039 .735 ±.030 .713 ±.055 .610 ±.024 .699 ±.060 .700 ±.036 .697 ±.039 .718 ±.005 4 .733 ±.041 .646 ±.019 .683 ±.022 .725 ±.037 .672 ±.010 .646 ±.019 .713 ±.018 .715 ±.047 .733 ±.034 .712 ±.042 .664 ±.015 .701 ±.018 .708 ±.025 .718 ±.045 .691 ±.009 5 .725 ±.025 .722 ±.014 .702 ±.025 .737 ±.038 .688 ±.025 .721 ±.013 .731 ±.029 .726 ±.025 .738 ±.030 .730 ±.045 .681 ±.015 .709 ±.028 .726 ±.043 .720 ±.028 .719 ±.020 hazelnut_spread contaminant_detection_10GHz 1 .935 ±.022 .922 ±.014 .925 ±.008 .917 ±.012 .895 ±.015 .918 ±.017 .938 ±.019 .926 ±.014 .919 ±.013 .921 ±.019 .934 ±.007 .875 ±.010 .930 ±.009 .932 ±.020 .920 ±.011 2 .961 ±.017 .957 ±.010 .958 ±.008 .950 ±.009 .922 ±.013 .956 ±.011 .968 ±.012 .954 ±.011 .950 ±.012 .951 ±.008 .966 ±.009 .938 ±.030 .966 ±.008 .956 ±.006 .958 ±.013 3 .976 ±.011 .965 ±.006 .975 ±.007 .963 ±.005 .945 ±.012 .968 ±.005 .976 ±.009 .976 ±.011 .961 ±.008 .968 ±.010 .977 ±.010 .963 ±.021 .977 ±.007 .970 ±.009 .978 ±.007 4 .981 ±.009 .976 ±.007 .978 ±.009 .978 ±.008 .963 ±.011 .976 ±.006 .982 ±.006 .979 ±.008 .976 ±.015 .977 ±.009 .982 ±.007 .976 ±.015 .980 ±.008 .978 ±.009 .980 ±.009 5 .982 ±.009 .981 ±.008 .980 ±.010 .982 ±.007 .977 ±.010 .982 ±.008 .982 ±.009 .981 ±.009 .982 ±.009 .980 ±.008 .983 ±.008 .977 ±.009 .982 ±.008 .982 ±.010 .982 ±.007 drug_induced autoimmunity_prediction 1 .638 ±.048 .625 ±.037 .612 ±.036 .637 ±.045 .546 ±.052 .625 ±.028 .614 ±.067 .653 ±.043 .598 ±.073 .602 ±.064 .576 ±.071 .603 ±.058 .584 ±.045 .631 ±.016 .653 ±.060 2 .689 ±.058 .646 ±.036 .637 ±.053 .699 ±.006 .607 ±.045 .671 ±.010 .717 ±.038 .692 ±.027 .640 ±.055 .651 ±.082 .643 ±.012 .679 ±.036 .550 ±.115 .712 ±.024 .718 ±.066 3 .766 ±.028 .753 ±.036 .711 ±.012 .778 ±.028 .670 ±.015 .689 ±.073 .767 ±.030 .774 ±.038 .712 ±.017 .753 ±.023 .773 ±.060 .741 ±.048 .754 ±.052 .788 ±.022 .732 ±.086 4 .775 ±.040 .755 ±.007 .755 ±.042 .806 ±.034 .779 ±.043 .763 ±.017 .788 ±.008 .794 ±.042 .803 ±.050 .783 ±.027 .769 ±.011 .769 ±.012 .759 ±.010 .790 ±.021 .773 ±.052 5 .805 ±.027 .781 ±.049 .780 ±.020 .787 ±.004 .797 ±.047 .767 ±.041 .787 ±.024 .794 ±.030 .805 ±.021 .747 ±.026 .823 ±.046 .760 ±.018 .787 ±.022 .785 ±.017 .798 ±.016 heart_disease switzerland 1 .617 ±.260 .529 ±.047 .810 ±.169 .685 ±.025 .467 ±.217 .529 ±.047 .643 ±.185 .580 ±.148 .707 ±.148 .498 ±.229 .888 ±.079 .507 ±.189 .600 ±.028 .587 ±.232 .421 ±.099 2 .553 ±.265 .439 ±.221 .759 ±.165 .644 ±.236 .695 ±.062 .439 ±.221 .732 ±.181 .457 ±.169 .835 ±.129 .750 ±.137 .812 ±.154 .586 ±.219 .465 ±.272 .791 ±.148 .454 ±.147 3 .813 ±.151 .443 ±.353 .773 ±.136 .597 ±.284 .652 ±.208 .443 ±.353 .660 ±.058 .478 ±.094 .809 ±.144 .818 ±.125 .778 ±.193 .583 ±.148 .729 ±.203 .730 ±.214 .399 ±.270 4 .816 ±.150 .343 ±.301 .737 ±.231 .667 ±.159 .427 ±.227 .343 ±.301 .723 ±.223 .464 ±.044 .804 ±.148 .725 ±.218 .751 ±.119 .652 ±.257 .695 ±.241 .758 ±.175 .718 ±.234 5 .762 ±.173 .649 ±.332 .750 ±.187 .633 ±.273 .586 ±.098 .649 ±.332 .820 ±.195 .731 ±.251 .766 ±.204 .771 ±.167 .806 ±.156 .757 ±.200 .740 ±.199 .724 ±.184 .605 ±.087 hr analytics 1 .717 ±.005 .783 ±.006 .783 ±.006 .766 ±.024 .646 ±.006 .783 ±.006 .690 ±.002 .783 ±.006 .719 ±.004 .783 ±.006 .717 ±.006 .582 ±.043 .783 ±.006 .717 ±.005 .782 ±.005 2 .783 ±.009 .790 ±.008 .790 ±.007 .785 ±.008 .670 ±.003 .790 ±.008 .699 ±.004 .786 ±.007 .787 ±.006 .791 ±.006 .764 ±.042 .716 ±.014 .792 ±.008 .729 ±.005 .790 ±.005 3 .790 ±.009 .793 ±.004 .789 ±.007 .789 ±.007 .696 ±.023 .793 ±.004 .736 ±.042 .793 ±.002 .791 ±.007 .788 ±.006 .788 ±.014 .789 ±.011 .795 ±.009 .791 ±.008 .795 ±.006 4 .796 ±.006 .794 ±.002 .794 ±.003 .798 ±.005 .709 ±.014 .794 ±.003 .767 ±.036 .798 ±.005 .799 ±.005 .790 ±.007 .795 ±.004 .793 ±.009 .797 ±.007 .795 ±.003 .798 ±.007 5 .798 ±.006 .796 ±.004 .796 ±.005 .801 ±.008 .795 ±.007 .796 ±.004 .798 ±.011 .800 ±.006 .801 ±.007 .794 ±.007 .797 ±.003 .801 ±.006 .800 ±.007 .799 ±.008 .800 ±.007 porto seguro 1 .613 ±.001 .616 ±.002 .606 ±.003 .605 ±.002 .549 ±.002 .606 ±.012 .605 ±.014 .615 ±.003 .593 ±.001 .600 ±.006 .602 ±.002 .596 ±.010 .593 ±.008 .612 ±.002 .578 ±.011 2 .618 ±.002 .626 ±.006 .621 ±.005 .622 ±.002 .597 ±.003 .617 ±.013 .616 ±.010 .624 ±.004 .617 ±.002 .615 ±.004 .617 ±.004 .613 ±.005 .614 ±.008 .618 ±.002 .604 ±.010 3 .623 ±.002 .631 ±.004 .628 ±.001 .628 ±.002 .609 ±.003 .626 ±.008 .622 ±.013 .628 ±.002 .626 ±.001 .618 ±.003 .622 ±.001 .617 ±.003 .620 ±.006 .629 ±.002 .619 ±.006 4 .635 ±.002 .635 ±.002 .632 ±.001 .631 ±.004 .621 ±.003 .630 ±.005 .627 ±.011 .632 ±.001 .635 ±.001 .625 ±.002 .626 ±.001 .628 ±.002 .629 ±.005 .634 ±.002 .629 ±.002 5 .637 ±.002 .635 ±.001 .637 ±.001 .634 ±.002 .629 ±.002 .632 ±.004 .633 ±.002 .635 ±.003 .637 ±.001 .634 ±.002 .633 ±.004 .636 ±.001 .631 ±.010 .637 ±.001 .632 ±.001 70 (continued from previous page) DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCOMarkovBlanketMImRMR(R)ReliefFRandomSBERFImportanceSFS japanese_credit screening 1 .822 ±.067 .717 ±.177 .809 ±.057 .783 ±.062 .687 ±.143 .817 ±.044 .699 ±.077 .794 ±.078 .731 ±.056 .833 ±.108 .717 ±.032 .580 ±.121 .695 ±.061 .791 ±.078 .697 ±.065 2 .744 ±.080 .788 ±.043 .698 ±.145 .690 ±.111 .720 ±.102 .790 ±.040 .755 ±.068 .680 ±.128 .698 ±.145 .812 ±.094 .744 ±.073 .665 ±.032 .680 ±.107 .689 ±.116 .701 ±.080 3 .716 ±.103 .792 ±.031 .706 ±.126 .713 ±.064 .704 ±.115 .725 ±.114 .734 ±.183 .738 ±.049 .704 ±.149 .811 ±.079 .695 ±.162 .667 ±.072 .734 ±.110 .729 ±.068 .796 ±.028 4 .759 ±.071 .752 ±.074 .734 ±.104 .731 ±.075 .702 ±.107 .753 ±.069 .737 ±.139 .742 ±.077 .730 ±.044 .829 ±.081 .761 ±.043 .730 ±.128 .715 ±.124 .752 ±.046 .718 ±.105 5 .767 ±.091 .770 ±.055 .777 ±.079 .744 ±.114 .774 ±.068 .756 ±.041 .735 ±.125 .748 ±.107 .774 ±.061 .818 ±.060 .775 ±.063 .770 ±.082 .744 ±.101 .760 ±.095 .750 ±.087 ionosphere 1 .945 ±.024 .911 ±.043 .953 ±.017 .943 ±.030 .905 ±.033 .911 ±.042 .931 ±.035 .945 ±.036 .954 ±.026 .950 ±.019 .955 ±.021 .948 ±.005 .939 ±.020 .959 ±.011 .956 ±.012 2 .954 ±.018 .936 ±.030 .975 ±.015 .954 ±.011 .938 ±.021 .941 ±.023 .954 ±.020 .965 ±.030 .963 ±.021 .952 ±.018 .968 ±.023 .962 ±.038 .954 ±.025 .961 ±.022 .961 ±.020 3 .968 ±.021 .962 ±.018 .968 ±.005 .968 ±.011 .937 ±.052 .968 ±.017 .962 ±.023 .983 ±.013 .965 ±.011 .964 ±.015 .962 ±.017 .970 ±.029 .967 ±.015 .974 ±.012 .954 ±.037 4 .967 ±.029 .970 ±.013 .970 ±.014 .973 ±.015 .968 ±.017 .966 ±.007 .972 ±.015 .974 ±.018 .980 ±.007 .965 ±.020 .959 ±.014 .953 ±.019 .963 ±.028 .968 ±.021 .960 ±.023 5 .971 ±.018 .967 ±.024 .974 ±.017 .973 ±.020 .977 ±.014 .972 ±.019 .966 ±.028 .973 ±.021 .974 ±.013 .965 ±.034 .967 ±.015 .967 ±.025 .967 ±.027 .969 ±.018 .972 ±.024 smoking lung_cancer 1 .654 ±.062 .672 ±.065 .712 ±.062 .668 ±.060 .658 ±.020 .674 ±.026 .644 ±.112 .684 ±.076 .689 ±.079 .693 ±.076 .705 ±.048 .557 ±.080 .556 ±.064 .726 ±.065 .621 ±.056 2 .771 ±.006 .735 ±.063 .711 ±.045 .735 ±.049 .733 ±.031 .734 ±.067 .636 ±.031 .708 ±.021 .717 ±.061 .710 ±.056 .743 ±.029 .646 ±.033 .666 ±.042 .730 ±.069 .693 ±.052 3 .739 ±.046 .788 ±.053 .704 ±.004 .723 ±.017 .725 ±.030 .752 ±.058 .711 ±.104 .732 ±.006 .718 ±.058 .724 ±.049 .727 ±.032 .688 ±.088 .705 ±.054 .722 ±.061 .736 ±.034 4 .738 ±.048 .766 ±.040 .736 ±.042 .740 ±.038 .774 ±.045 .766 ±.018 .741 ±.055 .747 ±.049 .714 ±.056 .759 ±.064 .753 ±.040 .735 ±.069 .747 ±.061 .761 ±.020 .747 ±.041 5 .758 ±.059 .763 ±.045 .753 ±.046 .745 ±.038 .763 ±.055 .769 ±.040 .747 ±.039 .750 ±.037 .763 ±.039 .736 ±.055 .769 ±.065 .736 ±.053 .766 ±.065 .746 ±.037 .734 ±.053 nci_pancreatic cancer 1 .611 ±.026 .681 ±.068 .626 ±.090 .598 ±.030 .579 ±.030 .559 ±.071 .612 ±.089 .551 ±.074 .576 ±.088 .568 ±.080 .615 ±.074 .441 ±.087 .472 ±.014 .615 ±.093 .506 ±.051 2 .634 ±.049 .772 ±.044 .660 ±.063 .624 ±.061 .574 ±.017 .720 ±.055 .603 ±.085 .623 ±.066 .509 ±.043 .714 ±.027 .648 ±.088 .582 ±.009 .521 ±.043 .667 ±.089 .572 ±.090 3 .692 ±.068 .764 ±.066 .652 ±.054 .624 ±.048 .486 ±.046 .768 ±.067 .660 ±.059 .620 ±.035 .596 ±.127 .733 ±.060 .672 ±.107 .679 ±.043 .651 ±.039 .737 ±.086 .655 ±.057 4 .683 ±.057 .737 ±.098 .644 ±.094 .654 ±.063 .559 ±.024 .762 ±.078 .663 ±.100 .661 ±.039 .635 ±.124 .732 ±.090 .674 ±.081 .648 ±.101 .651 ±.017 .741 ±.066 .650 ±.073 5 .695 ±.075 .712 ±.091 .652 ±.117 .705 ±.062 .583 ±.033 .719 ±.058 .665 ±.088 .640 ±.019 .671 ±.080 .715 ±.063 .675 ±.070 .684 ±.062 .689 ±.091 .713 ±.066 .670 ±.061 homesite_quote conversion 1 .902 ±.001 .816 ±.002 .875 ±.001 .710 ±.091 .501 ±.001 .816 ±.002 .723 ±.116 .880 ±.019 .890 ±.001 .902 ±.000 .861 ±.037 .599 ±.040 .603 ±.021 .903 ±.001 .848 ±.015 2 .944 ±.005 .929 ±.001 .885 ±.001 .871 ±.030 .610 ±.091 .929 ±.001 .863 ±.031 .920 ±.001 .909 ±.000 .908 ±.001 .920 ±.001 .724 ±.064 .724 ±.053 .912 ±.001 .842 ±.035 3 .957 ±.002 .951 ±.001 .909 ±.001 .916 ±.000 .673 ±.002 .950 ±.000 .871 ±.030 .932 ±.000 .928 ±.002 .923 ±.001 .939 ±.001 .773 ±.056 .779 ±.055 .955 ±.001 .881 ±.024 4 .962 ±.001 .963 ±.000 .955 ±.000 .932 ±.000 .696 ±.001 .959 ±.003 .908 ±.024 .936 ±.003 .941 ±.000 .939 ±.005 .946 ±.001 .867 ±.026 .855 ±.008 .960 ±.001 .905 ±.017 5 .964 ±.001 .966 ±.001 .961 ±.001 .942 ±.000 .751 ±.006 .966 ±.000 .929 ±.006 .945 ±.002 .965 ±.000 .964 ±.000 .960 ±.001 .947 ±.009 .926 ±.023 .962 ±.001 .939 ±.006 heart_disease va_long_beach 1 .609 ±.049 .658 ±.143 .666 ±.107 .655 ±.106 .691 ±.104 .649 ±.132 .634 ±.102 .659 ±.109 .643 ±.128 .654 ±.108 .634 ±.015 .564 ±.134 .639 ±.062 .586 ±.071 .636 ±.078 2 .617 ±.102 .631 ±.083 .659 ±.090 .666 ±.101 .583 ±.132 .638 ±.094 .598 ±.066 .592 ±.034 .621 ±.075 .532 ±.045 .585 ±.121 .684 ±.064 .687 ±.137 .650 ±.102 .625 ±.083 3 .659 ±.134 .628 ±.096 .625 ±.068 .606 ±.053 .613 ±.096 .634 ±.103 .581 ±.052 .640 ±.106 .654 ±.082 .612 ±.100 .560 ±.098 .671 ±.093 .661 ±.070 .679 ±.077 .606 ±.108 4 .665 ±.093 .568 ±.184 .628 ±.053 .625 ±.137 .656 ±.144 .571 ±.186 .596 ±.018 .661 ±.087 .638 ±.099 .572 ±.066 .642 ±.068 .615 ±.068 .641 ±.113 .664 ±.101 .641 ±.090 5 .659 ±.129 .632 ±.137 .672 ±.117 .648 ±.112 .632 ±.083 .623 ±.128 .653 ±.074 .644 ±.089 .664 ±.122 .663 ±.112 .652 ±.085 .678 ±.074 .678 ±.060 .667 ±.076 .661 ±.104 qsar_biodeg 1 .900 ±.021 .897 ±.007 .874 ±.013 .898 ±.003 .680 ±.059 .887 ±.007 .882 ±.024 .912 ±.008 .878 ±.022 .911 ±.006 .907 ±.006 .878 ±.013 .907 ±.022 .866 ±.018 .915 ±.019 2 .927 ±.011 .935 ±.007 .916 ±.008 .916 ±.011 .892 ±.004 .921 ±.015 .924 ±.007 .925 ±.009 .918 ±.008 .911 ±.017 .927 ±.009 .909 ±.009 .925 ±.007 .921 ±.010 .920 ±.020 3 .928 ±.007 .931 ±.007 .928 ±.006 .925 ±.010 .915 ±.003 .924 ±.001 .925 ±.011 .922 ±.007 .924 ±.009 .921 ±.008 .925 ±.011 .922 ±.019 .930 ±.002 .925 ±.006 .927 ±.008 4 .923 ±.002 .934 ±.002 .934 ±.007 .925 ±.008 .923 ±.009 .932 ±.004 .926 ±.009 .927 ±.008 .929 ±.010 .931 ±.004 .927 ±.003 .920 ±.014 .934 ±.005 .928 ±.005 .932 ±.010 5 .929 ±.007 .937 ±.005 .935 ±.008 .928 ±.009 .929 ±.011 .934 ±.007 .930 ±.006 .933 ±.004 .929 ±.011 .933 ±.007 .936 ±.007 .929 ±.013 .931 ±.005 .933 ±.009 .934 ±.008 naticusdroid_android permissions_dataset 1 .979 ±.001 .977 ±.005 .979 ±.001 .850 ±.052 .578 ±.001 .970 ±.011 .973 ±.005 .781 ±.092 .975 ±.005 .850 ±.052 .973 ±.002 .886 ±.067 .826 ±.092 .979 ±.001 .974 ±.002 2 .983 ±.001 .983 ±.002 .981 ±.002 .938 ±.002 .632 ±.005 .981 ±.004 .978 ±.004 .879 ±.046 .981 ±.001 .938 ±.002 .980 ±.000 .888 ±.010 .897 ±.011 .983 ±.001 .977 ±.004 3 .984 ±.001 .984 ±.002 .984 ±.002 .957 ±.010 .723 ±.005 .984 ±.002 .979 ±.004 .955 ±.005 .984 ±.002 .957 ±.010 .983 ±.001 .950 ±.008 .922 ±.043 .984 ±.002 .981 ±.005 4 .985 ±.002 .985 ±.002 .984 ±.002 .978 ±.004 .823 ±.002 .985 ±.002 .981 ±.004 .968 ±.001 .985 ±.003 .978 ±.004 .984 ±.001 .958 ±.028 .976 ±.010 .985 ±.002 .983 ±.002 5 .985 ±.002 .985 ±.003 .984 ±.002 .984 ±.001 .956 ±.007 .985 ±.002 .982 ±.003 .981 ±.004 .984 ±.002 .984 ±.001 .984 ±.001 .977 ±.006 .983 ±.004 .985 ±.002 .983 ±.004 framingham heart_study 1 .647 ±.055 .710 ±.022 .699 ±.019 .697 ±.020 .563 ±.017 .706 ±.022 .692 ±.023 .687 ±.027 .696 ±.017 .698 ±.010 .687 ±.021 .597 ±.032 .702 ±.013 .664 ±.038 .691 ±.014 2 .687 ±.022 .718 ±.019 .699 ±.017 .700 ±.019 .588 ±.003 .718 ±.019 .714 ±.031 .690 ±.021 .697 ±.020 .714 ±.016 .704 ±.029 .659 ±.023 .714 ±.025 .704 ±.015 .700 ±.011 3 .717 ±.027 .722 ±.023 .718 ±.020 .712 ±.037 .664 ±.017 .722 ±.023 .722 ±.026 .719 ±.026 .703 ±.018 .717 ±.022 .715 ±.031 .683 ±.012 .725 ±.023 .718 ±.028 .717 ±.015 4 .717 ±.021 .719 ±.026 .725 ±.018 .710 ±.045 .720 ±.020 .716 ±.026 .721 ±.030 .718 ±.035 .707 ±.026 .715 ±.020 .717 ±.029 .720 ±.026 .725 ±.023 .721 ±.027 .716 ±.011 5 .727 ±.026 .721 ±.027 .727 ±.021 .723 ±.030 .716 ±.025 .716 ±.023 .724 ±.026 .720 ±.028 .717 ±.025 .709 ±.030 .722 ±.019 .699 ±.028 .720 ±.026 .721 ±.020 .718 ±.030 Table D.9: Per-cell results with downstream model TabICLv2. Tasks are grouped by metric. Each cell is the mean over three seeds, with the sample standard deviation as a subscript. DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCO MarkovBlanket MImRMR(R)ReliefFRandomSBERFImportanceSFS log-loss (↓) horse_colic survival 1 .770 ±.066 .743 ±.100 .740 ±.062 .790 ±.059 .834 ±.071 .743 ±.100 .781 ±.102 .764 ±.024 .750 ±.076 .781 ±.034 .786 ±.052 .798 ±.093 .815 ±.098 .760 ±.074 .833 ±.038 2 .731 ±.031 .720 ±.087 .736 ±.056 .744 ±.029 .804 ±.079 .714 ±.047 .743 ±.033 .704 ±.034 .744 ±.077 .733 ±.015 .742 ±.068 .754 ±.072 .772 ±.082 .712 ±.057 .791 ±.058 3 .713 ±.034 .724 ±.066 .723 ±.065 .723 ±.041 .769 ±.063 .716 ±.058 .715 ±.039 .693 ±.035 .729 ±.080 .707 ±.020 .725 ±.033 .713 ±.043 .744 ±.054 .705 ±.025 .760 ±.072 4 .709 ±.008 .702 ±.032 .714 ±.062 .689 ±.015 .698 ±.040 .702 ±.040 .708 ±.075 .690 ±.019 .692 ±.031 .703 ±.037 .688 ±.009 .740 ±.037 .678 ±.018 .696 ±.028 .756 ±.055 5 .700 ±.025 .709 ±.037 .713 ±.067 .699 ±.026 .682 ±.014 .709 ±.046 .708 ±.074 .685 ±.024 .695 ±.018 .702 ±.034 .685 ±.023 .709 ±.062 .685 ±.028 .695 ±.029 .726 ±.061 srbct prediction 1 .848 ±.410 .237 ±.111 .530 ±.242 .601 ±.285 1.53 ±.24 .340 ±.313 1.24 ±.13 .626 ±.159 .568 ±.320 .602 ±.554 .309 ±.211 .907 ±.392 1.03 ±.35 .358 ±.265 1.14 ±.31 2 .487 ±.197 .126 ±.112 .140 ±.081 .334 ±.132 1.09 ±.31 .0701 ±.0555 .573 ±.256 .204 ±.166 .197 ±.205 .309 ±.109 .124 ±.047 1.33 ±.29 1.31 ±.31 .0491 ±.0521 .616 ±.144 3 .164 ±.055 .0167 ±.0037 .0476 ±.0376 .0609 ±.0427 .657 ±.276 .0201 ±.0179 .296 ±.099 .133 ±.105 .0893 ±.0785 .0555 ±.0422 .0411 ±.0302 .187 ±.082 .317 ±.059 .0370 ±.0024 .245 ±.075 4 .0908 ±.0688 .0255 ±.0107 .0306 ±.0281 .0204 ±.0076 .191 ±.071 .0197 ±.0151 .0678 ±.0514 .0299 ±.0036 .0160 ±.0039 .0299 ±.0170 .0173 ±.0067 .223 ±.122 .211 ±.107 .0157 ±.0062 .142 ±.094 5 .0787 ±.0424 .0306 ±.0135 .0503 ±.0226 .0392 ±.0114 .0662 ±.0261 .0398 ±.0214 .0633 ±.0410 .0377 ±.0100 .0394 ±.0141 .0509 ±.0185 .0402 ±.0142 .0661 ±.0390 .0576 ±.0435 .0244 ±.0101 .0723 ±.0363 71 (continued from previous page) DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCO MarkovBlanket MImRMR(R)ReliefFRandomSBERFImportanceSFS sdss_17 1 .110 ±.002 .127 ±.015 .154 ±.002 .156 ±.002 .644 ±.001 .250 ±.241 .111 ±.001 .111 ±.001 .144 ±.001 .172 ±.003 .170 ±.002 .528 ±.388 .110 ±.002 .156 ±.002 .144 ±.001 2 .0801 ±.0022 .0818 ±.0026 .0907 ±.0033 .0831 ±.0011 .431 ±.145 .0808 ±.0020 .0798 ±.0008 .0849 ±.0066 .0835 ±.0008 .165 ±.001 .0942 ±.0030 .106 ±.014 .0813 ±.0042 .0820 ±.0009 .0978 ±.0012 3 .0742 ±.0014 .0729 ±.0006 .0827 ±.0093 .0744 ±.0014 .342 ±.046 .0729 ±.0005 .0742 ±.0006 .0743 ±.0015 .0744 ±.0014 .0960 ±.0007 .0833 ±.0059 .328 ±.060 .0742 ±.0004 .0742 ±.0015 .0770 ±.0014 4 .0698 ±.0010 .0712 ±.0008 .0772 ±.0044 .0697 ±.0010 .142 ±.098 .0715 ±.0005 .0710 ±.0017 .0697 ±.0010 .0702 ±.0007 .0860 ±.0011 .0710 ±.0010 .209 ±.112 .0717 ±.0006 .0698 ±.0010 .0710 ±.0003 5 .0701 ±.0012 .0717 ±.0002 .0712 ±.0014 .0702 ±.0010 .0729 ±.0030 .0716 ±.0005 .0702 ±.0011 .0702 ±.0011 .0703 ±.0010 .0740 ±.0012 .0701 ±.0011 .133 ±.108 .0701 ±.0013 .0701 ±.0011 .0702 ±.0011 predict_students_dropout and_academic_success 1 .615 ±.033 .579 ±.017 .632 ±.018 .652 ±.009 .970 ±.014 .579 ±.020 .590 ±.044 .583 ±.010 .623 ±.014 .671 ±.022 .583 ±.019 .674 ±.027 .652 ±.035 .653 ±.008 .683 ±.044 2 .557 ±.012 .550 ±.024 .556 ±.019 .579 ±.014 .879 ±.032 .547 ±.029 .564 ±.015 .573 ±.012 .544 ±.018 .616 ±.014 .554 ±.014 .617 ±.018 .613 ±.040 .559 ±.014 .614 ±.029 3 .540 ±.012 .533 ±.018 .545 ±.028 .574 ±.009 .663 ±.028 .536 ±.014 .546 ±.019 .571 ±.007 .533 ±.015 .585 ±.012 .534 ±.020 .647 ±.019 .539 ±.011 .546 ±.014 .582 ±.013 4 .536 ±.017 .531 ±.012 .534 ±.015 .568 ±.004 .573 ±.010 .530 ±.013 .537 ±.021 .567 ±.005 .534 ±.013 .577 ±.008 .533 ±.017 .575 ±.020 .540 ±.018 .528 ±.013 .549 ±.012 5 .530 ±.014 .527 ±.014 .532 ±.013 .551 ±.022 .529 ±.007 .530 ±.012 .539 ±.021 .564 ±.007 .534 ±.013 .549 ±.012 .531 ±.015 .540 ±.025 .527 ±.013 .528 ±.015 .535 ±.016 ancestry study 1 1.62 ±.08 1.73 ±.08 1.71 ±.06 2.03 ±.15 2.10 ±.02 1.85 ±.23 1.76 ±.10 1.71 ±.07 1.69 ±.07 1.70 ±.06 1.71 ±.05 2.05 ±.14 2.11 ±.03 1.70 ±.06 1.82 ±.05 2 1.58 ±.10 1.64 ±.04 1.66 ±.08 1.82 ±.16 2.10 ±.01 1.78 ±.15 1.72 ±.10 1.67 ±.07 1.59 ±.09 1.69 ±.06 1.64 ±.06 2.08 ±.05 2.09 ±.06 1.70 ±.04 1.80 ±.04 3 1.59 ±.14 1.64 ±.05 1.56 ±.07 1.68 ±.06 1.97 ±.07 1.62 ±.06 1.70 ±.10 1.62 ±.05 1.51 ±.07 1.62 ±.05 1.61 ±.08 2.06 ±.04 1.97 ±.11 1.61 ±.12 1.75 ±.06 4 1.54 ±.11 1.60 ±.07 1.56 ±.07 1.61 ±.05 1.86 ±.03 1.57 ±.06 1.61 ±.06 1.62 ±.06 1.49 ±.05 1.61 ±.06 1.57 ±.11 1.80 ±.26 1.86 ±.17 1.50 ±.05 1.73 ±.05 5 1.46 ±.07 1.53 ±.04 1.43 ±.05 1.57 ±.06 1.53 ±.06 1.50 ±.08 1.53 ±.09 1.58 ±.06 1.44 ±.05 1.56 ±.07 1.51 ±.09 1.81 ±.12 1.70 ±.18 1.46 ±.08 1.59 ±.09 otto_group_product classification_challenge 1 .671 ±.010 .759 ±.028 .880 ±.017 1.18 ±.00 .886 ±.030 .840 ±.006 .891 ±.043 .679 ±.005 .892 ±.014 .789 ±.006 .911 ±.014 1.10 ±.11 1.09 ±.09 .672 ±.004 1.11 ±.15 2 .507 ±.008 .551 ±.011 .604 ±.017 .829 ±.007 .599 ±.012 .575 ±.018 .714 ±.011 .521 ±.004 .586 ±.006 .578 ±.008 .585 ±.013 .778 ±.014 .744 ±.025 .512 ±.008 .755 ±.090 3 .450 ±.005 .473 ±.004 .505 ±.007 .556 ±.002 .480 ±.004 .478 ±.002 .594 ±.036 .460 ±.009 .512 ±.006 .498 ±.005 .474 ±.008 .586 ±.020 .571 ±.036 .461 ±.011 .588 ±.033 4 .428 ±.004 .438 ±.004 .457 ±.003 .489 ±.005 .444 ±.002 .439 ±.005 .521 ±.016 .437 ±.005 .452 ±.005 .449 ±.003 .446 ±.005 .508 ±.018 .509 ±.034 .432 ±.006 .526 ±.038 5 .421 ±.004 .426 ±.006 .427 ±.006 .433 ±.004 .425 ±.004 .426 ±.006 .455 ±.009 .423 ±.005 .428 ±.006 .428 ±.002 .424 ±.003 .450 ±.015 .466 ±.010 .421 ±.005 .460 ±.018 lung_cancer 1 .401 ±.166 .377 ±.047 .471 ±.125 .235 ±.112 .762 ±.091 .514 ±.146 .621 ±.139 .269 ±.089 .376 ±.065 .321 ±.114 .311 ±.076 .696 ±.089 .673 ±.042 .322 ±.172 .517 ±.118 2 .235 ±.100 .219 ±.046 .366 ±.148 .176 ±.051 .724 ±.135 .302 ±.058 .315 ±.110 .169 ±.079 .147 ±.048 .168 ±.023 .202 ±.056 .386 ±.097 .445 ±.040 .188 ±.058 .332 ±.138 3 .190 ±.025 .160 ±.053 .182 ±.028 .152 ±.064 .507 ±.093 .233 ±.058 .177 ±.036 .187 ±.097 .156 ±.063 .148 ±.044 .141 ±.013 .161 ±.019 .189 ±.051 .143 ±.026 .215 ±.058 4 .145 ±.038 .141 ±.051 .148 ±.031 .133 ±.029 .380 ±.039 .168 ±.048 .140 ±.031 .135 ±.033 .141 ±.036 .136 ±.045 .141 ±.028 .133 ±.036 .130 ±.056 .123 ±.027 .129 ±.048 5 .168 ±.057 .159 ±.060 .134 ±.018 .127 ±.024 .214 ±.040 .168 ±.069 .147 ±.007 .136 ±.030 .142 ±.037 .159 ±.035 .121 ±.024 .149 ±.043 .150 ±.014 .164 ±.053 .146 ±.027 soybean large 1 .514 ±.126 1.26 ±.09 1.09 ±.28 1.17 ±.12 1.14 ±.27 1.15 ±.23 1.07 ±.17 .517 ±.018 .633 ±.080 .470 ±.075 1.30 ±.08 1.20 ±.21 1.07 ±.38 .411 ±.075 .655 ±.159 2 .254 ±.043 .823 ±.054 .678 ±.052 .530 ±.129 .532 ±.284 .819 ±.126 .608 ±.110 .373 ±.032 .336 ±.027 .286 ±.007 .801 ±.107 .624 ±.217 .397 ±.042 .202 ±.071 .436 ±.071 3 .210 ±.071 .436 ±.052 .526 ±.159 .371 ±.131 .342 ±.072 .601 ±.158 .522 ±.111 .318 ±.057 .197 ±.025 .213 ±.014 .290 ±.042 .259 ±.003 .434 ±.177 .205 ±.078 .346 ±.178 4 .166 ±.069 .278 ±.051 .348 ±.121 .306 ±.152 .282 ±.116 .333 ±.035 .311 ±.058 .255 ±.136 .187 ±.038 .190 ±.028 .244 ±.041 .170 ±.036 .281 ±.112 .175 ±.078 .241 ±.067 5 .164 ±.078 .187 ±.051 .241 ±.116 .170 ±.091 .228 ±.126 .249 ±.034 .218 ±.052 .204 ±.090 .181 ±.040 .138 ±.042 .216 ±.061 .189 ±.068 .213 ±.099 .158 ±.071 .190 ±.113 mic 1 .489 ±.015 .525 ±.011 .522 ±.024 .548 ±.008 .703 ±.018 .525 ±.032 .601 ±.083 .499 ±.003 .539 ±.020 .519 ±.026 .539 ±.015 .700 ±.011 .675 ±.033 .530 ±.034 .528 ±.024 2 .483 ±.028 .497 ±.016 .484 ±.006 .548 ±.008 .695 ±.013 .509 ±.039 .591 ±.077 .493 ±.005 .539 ±.021 .493 ±.036 .541 ±.018 .688 ±.022 .670 ±.030 .495 ±.016 .527 ±.024 3 .474 ±.019 .474 ±.011 .482 ±.010 .513 ±.027 .686 ±.019 .476 ±.015 .546 ±.068 .487 ±.012 .477 ±.008 .457 ±.015 .530 ±.019 .543 ±.013 .536 ±.036 .470 ±.027 .500 ±.022 4 .465 ±.007 .462 ±.005 .474 ±.007 .494 ±.007 .674 ±.010 .466 ±.001 .503 ±.025 .453 ±.007 .447 ±.010 .449 ±.004 .498 ±.013 .517 ±.073 .484 ±.008 .458 ±.012 .504 ±.037 5 .441 ±.010 .444 ±.005 .448 ±.006 .457 ±.018 .618 ±.017 .448 ±.015 .454 ±.006 .438 ±.003 .439 ±.010 .455 ±.002 .461 ±.015 .470 ±.014 .481 ±.024 .436 ±.005 .451 ±.013 ljubljana primary_tumor 1 1.79 ±.17 1.91 ±.14 1.85 ±.04 1.78 ±.10 2.03 ±.01 1.83 ±.09 1.85 ±.17 1.89 ±.15 1.79 ±.13 1.86 ±.14 1.90 ±.12 1.88 ±.13 1.80 ±.12 1.86 ±.14 1.81 ±.10 2 1.63 ±.17 1.73 ±.13 1.67 ±.07 1.64 ±.23 1.86 ±.13 1.71 ±.13 1.78 ±.17 1.68 ±.19 1.54 ±.20 1.65 ±.23 1.66 ±.08 1.81 ±.09 1.75 ±.20 1.67 ±.14 1.72 ±.16 3 1.45 ±.14 1.63 ±.14 1.51 ±.09 1.55 ±.21 1.77 ±.07 1.57 ±.14 1.52 ±.07 1.51 ±.14 1.42 ±.11 1.55 ±.21 1.53 ±.11 1.64 ±.20 1.60 ±.12 1.45 ±.16 1.61 ±.17 4 1.37 ±.13 1.53 ±.09 1.38 ±.10 1.46 ±.18 1.60 ±.13 1.47 ±.13 1.46 ±.12 1.47 ±.16 1.35 ±.11 1.46 ±.18 1.43 ±.07 1.54 ±.03 1.42 ±.14 1.35 ±.10 1.47 ±.15 5 1.32 ±.10 1.38 ±.13 1.35 ±.08 1.36 ±.14 1.42 ±.09 1.34 ±.10 1.36 ±.08 1.39 ±.14 1.32 ±.09 1.36 ±.14 1.39 ±.12 1.39 ±.18 1.38 ±.12 1.32 ±.10 1.37 ±.11 anneal 1 .0667 ±.0191 .114 ±.058 .229 ±.012 .0602 ±.0214 .527 ±.016 .126 ±.029 .109 ±.033 .0491 ±.0185 .0733 ±.0055 .0729 ±.0330 .129 ±.017 .562 ±.061 .0666 ±.0182 .0575 ±.0180 .0615 ±.0208 2 .0385 ±.0255 .0329 ±.0155 .0921 ±.0490 .0282 ±.0108 .430 ±.007 .0247 ±.0079 .0484 ±.0371 .0308 ±.0125 .0395 ±.0225 .0266 ±.0123 .0563 ±.0115 .223 ±.101 .0331 ±.0112 .0212 ±.0100 .0360 ±.0176 3 .0386 ±.0214 .0220 ±.0129 .0349 ±.0059 .0227 ±.0120 .307 ±.025 .0197 ±.0099 .0417 ±.0415 .0255 ±.0093 .0248 ±.0106 .0234 ±.0127 .0238 ±.0112 .0902 ±.0290 .0246 ±.0106 .0261 ±.0064 .0381 ±.0183 4 .0408 ±.0262 .0229 ±.0115 .0272 ±.0102 .0245 ±.0131 .194 ±.033 .0203 ±.0102 .0272 ±.0157 .0244 ±.0059 .0232 ±.0091 .0250 ±.0133 .0243 ±.0108 .106 ±.038 .0239 ±.0077 .0216 ±.0059 .0330 ±.0144 5 .0237 ±.0064 .0247 ±.0089 .0190 ±.0059 .0223 ±.0061 .0286 ±.0057 .0202 ±.0077 .0276 ±.0157 .0251 ±.0054 .0242 ±.0056 .0231 ±.0056 .0253 ±.0078 .0204 ±.0103 .0244 ±.0053 .0216 ±.0077 .0276 ±.0093 audiology diagnosis 1 .453 ±.193 .537 ±.182 .493 ±.112 .456 ±.206 .535 ±.094 .591 ±.103 .586 ±.095 .453 ±.200 .436 ±.176 .505 ±.133 .436 ±.154 .560 ±.114 .429 ±.032 .421 ±.173 .468 ±.112 2 .440 ±.184 .451 ±.162 .356 ±.114 .418 ±.215 .386 ±.142 .551 ±.054 .541 ±.095 .418 ±.189 .380 ±.136 .434 ±.130 .405 ±.138 .504 ±.241 .428 ±.049 .380 ±.218 .414 ±.163 3 .372 ±.235 .396 ±.172 .311 ±.132 .408 ±.186 .344 ±.062 .430 ±.123 .472 ±.074 .390 ±.202 .362 ±.134 .374 ±.118 .320 ±.139 .366 ±.105 .392 ±.028 .313 ±.197 .422 ±.166 4 .335 ±.186 .331 ±.164 .300 ±.150 .340 ±.179 .322 ±.119 .362 ±.144 .454 ±.133 .323 ±.080 .326 ±.168 .339 ±.132 .312 ±.176 .349 ±.139 .370 ±.034 .257 ±.168 .388 ±.212 5 .301 ±.211 .331 ±.139 .296 ±.151 .316 ±.212 .315 ±.136 .306 ±.170 .426 ±.159 .280 ±.131 .300 ±.174 .312 ±.172 .299 ±.171 .327 ±.155 .365 ±.086 .271 ±.161 .357 ±.198 RMSE (↓) qsar_tid_11 1 1.36 ±.02 1.42 ±.03 1.39 ±.03 1.53 ±.02 1.40 ±.03 1.44 ±.03 1.48 ±.07 1.53 ±.01 1.36 ±.05 1.53 ±.02 1.49 ±.01 1.53 ±.00 1.53 ±.01 1.33 ±.03 1.38 ±.03 2 1.23 ±.01 1.27 ±.04 1.31 ±.03 1.50 ±.01 1.32 ±.00 1.26 ±.04 1.38 ±.01 1.47 ±.06 1.25 ±.02 1.50 ±.01 1.33 ±.05 1.50 ±.02 1.50 ±.03 1.21 ±.02 1.33 ±.03 3 .974 ±.026 1.02 ±.02 1.02 ±.02 1.36 ±.04 1.15 ±.02 1.01 ±.02 1.17 ±.03 1.31 ±.04 .983 ±.017 1.36 ±.04 .989 ±.026 1.29 ±.02 1.28 ±.02 .955 ±.033 1.25 ±.06 4 .758 ±.020 .792 ±.035 .795 ±.023 .951 ±.055 .893 ±.007 .786 ±.023 .893 ±.043 .942 ±.017 .793 ±.013 .951 ±.055 .750 ±.028 .911 ±.063 .938 ±.083 .743 ±.026 .870 ±.012 5 .724 ±.025 .742 ±.038 .742 ±.030 .774 ±.020 .753 ±.020 .744 ±.036 .770 ±.020 .785 ±.046 .737 ±.036 .774 ±.020 .716 ±.027 .763 ±.034 .758 ±.023 .722 ±.031 .770 ±.030 forest fires 1 1.40 ±.03 1.41 ±.02 1.42 ±.04 1.40 ±.03 1.40 ±.04 1.41 ±.02 1.40 ±.03 1.41 ±.04 1.41 ±.04 1.41 ±.05 1.41 ±.04 1.41 ±.04 1.40 ±.02 1.40 ±.03 1.39 ±.03 2 1.40 ±.03 1.41 ±.03 1.42 ±.05 1.40 ±.04 1.40 ±.05 1.41 ±.03 1.40 ±.03 1.40 ±.02 1.41 ±.04 1.41 ±.05 1.41 ±.05 1.40 ±.03 1.40 ±.02 1.42 ±.05 1.40 ±.03 3 1.41 ±.04 1.41 ±.03 1.41 ±.03 1.40 ±.04 1.40 ±.05 1.41 ±.03 1.40 ±.03 1.40 ±.02 1.40 ±.03 1.40 ±.03 1.40 ±.04 1.41 ±.03 1.40 ±.02 1.40 ±.03 1.41 ±.04 4 1.40 ±.03 1.41 ±.03 1.40 ±.03 1.39 ±.03 1.40 ±.04 1.41 ±.03 1.39 ±.03 1.40 ±.04 1.39 ±.03 1.40 ±.03 1.40 ±.04 1.40 ±.03 1.40 ±.03 1.40 ±.02 1.40 ±.04 5 1.40 ±.03 1.40 ±.02 1.40 ±.03 1.39 ±.03 1.39 ±.03 1.40 ±.02 1.40 ±.03 1.40 ±.03 1.40 ±.03 1.40 ±.03 1.40 ±.03 1.40 ±.03 1.40 ±.03 1.40 ±.03 1.40 ±.03 allstate claims_severity 1 .668 ±.002 .680 ±.002 .675 ±.002 .705 ±.010 .812 ±.002 .680 ±.002 .669 ±.001 .694 ±.000 .670 ±.002 .716 ±.013 .695 ±.002 .783 ±.024 .787 ±.024 .668 ±.002 .666 ±.005 2 .638 ±.003 .651 ±.002 .659 ±.001 .648 ±.003 .809 ±.003 .651 ±.002 .634 ±.006 .636 ±.001 .645 ±.001 .662 ±.014 .665 ±.022 .786 ±.016 .793 ±.021 .640 ±.004 .651 ±.004 3 .589 ±.002 .604 ±.005 .642 ±.001 .599 ±.011 .808 ±.002 .604 ±.005 .605 ±.006 .614 ±.001 .615 ±.002 .616 ±.001 .627 ±.007 .732 ±.025 .737 ±.015 .589 ±.002 .633 ±.016 4 .560 ±.004 .563 ±.004 .611 ±.008 .571 ±.002 .781 ±.009 .563 ±.004 .566 ±.007 .614 ±.001 .568 ±.003 .604 ±.002 .596 ±.010 .672 ±.036 .673 ±.031 .559 ±.001 .607 ±.023 5 .545 ±.001 .543 ±.001 .556 ±.003 .563 ±.001 .684 ±.011 .543 ±.001 .545 ±.004 .556 ±.001 .546 ±.002 .584 ±.009 .567 ±.008 .600 ±.015 .609 ±.019 .546 ±.000 .579 ±.007 72 (continued from previous page) DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCO MarkovBlanket MImRMR(R)ReliefFRandomSBERFImportanceSFS miami housing 1 .239 ±.020 .255 ±.005 .319 ±.002 .265 ±.007 .301 ±.006 .255 ±.005 .337 ±.050 .179 ±.006 .186 ±.018 .310 ±.004 .366 ±.001 .429 ±.088 .182 ±.014 .245 ±.026 .257 ±.006 2 .167 ±.004 .205 ±.005 .225 ±.004 .164 ±.003 .217 ±.003 .205 ±.005 .204 ±.043 .174 ±.005 .176 ±.004 .164 ±.007 .225 ±.019 .279 ±.093 .147 ±.013 .167 ±.005 .158 ±.006 3 .140 ±.011 .153 ±.009 .152 ±.006 .159 ±.003 .212 ±.003 .151 ±.006 .135 ±.008 .165 ±.002 .159 ±.004 .151 ±.005 .153 ±.020 .183 ±.027 .131 ±.006 .140 ±.012 .133 ±.006 4 .137 ±.008 .144 ±.004 .132 ±.006 .149 ±.004 .213 ±.003 .144 ±.004 .129 ±.005 .151 ±.005 .149 ±.004 .135 ±.013 .131 ±.006 .165 ±.033 .130 ±.005 .134 ±.005 .130 ±.005 5 .130 ±.006 .130 ±.006 .129 ±.005 .130 ±.006 .183 ±.003 .130 ±.006 .128 ±.006 .130 ±.006 .130 ±.006 .129 ±.006 .130 ±.006 .146 ±.019 .128 ±.005 .130 ±.006 .128 ±.005 wine quality 1 .727 ±.007 .757 ±.010 .753 ±.006 .753 ±.006 .809 ±.010 .757 ±.010 .822 ±.008 .727 ±.007 .753 ±.006 .772 ±.004 .753 ±.022 .778 ±.032 .745 ±.011 .727 ±.007 .772 ±.004 2 .641 ±.009 .658 ±.008 .665 ±.009 .670 ±.013 .708 ±.006 .658 ±.008 .705 ±.018 .654 ±.007 .678 ±.016 .698 ±.011 .665 ±.017 .698 ±.042 .645 ±.017 .637 ±.008 .680 ±.008 3 .609 ±.007 .635 ±.009 .644 ±.008 .622 ±.006 .630 ±.006 .635 ±.009 .623 ±.019 .619 ±.013 .630 ±.014 .628 ±.014 .615 ±.009 .643 ±.027 .613 ±.012 .608 ±.007 .619 ±.008 4 .598 ±.009 .608 ±.003 .611 ±.005 .603 ±.012 .613 ±.009 .608 ±.003 .604 ±.008 .600 ±.009 .604 ±.015 .607 ±.008 .600 ±.008 .620 ±.009 .603 ±.008 .596 ±.010 .603 ±.007 5 .592 ±.013 .595 ±.008 .604 ±.011 .595 ±.012 .599 ±.010 .595 ±.008 .596 ±.012 .596 ±.011 .595 ±.016 .598 ±.010 .592 ±.010 .602 ±.011 .593 ±.012 .594 ±.012 .599 ±.012 body_density prediction 1 .0108 ±.0003 .0105 ±.0013 .0108 ±.0007 .0107 ±.0009 .0117 ±.0028 .0105 ±.0013 .0119 ±.0024 .0106 ±.0009 .0108 ±.0010 .0109 ±.0013 .0105 ±.0012 .0146 ±.0043 .0108 ±.0011 .0105 ±.0008 .0104 ±.0007 2 .0104 ±.0006 .0104 ±.0012 .0105 ±.0007 .0106 ±.0008 .0106 ±.0008 .0104 ±.0012 .0121 ±.0029 .0105 ±.0007 .0106 ±.0008 .0106 ±.0008 .0104 ±.0010 .0127 ±.0018 .0106 ±.0013 .0104 ±.0010 .0105 ±.0010 3 .0104 ±.0008 .0103 ±.0013 .0107 ±.0007 .0107 ±.0010 .0107 ±.0012 .0103 ±.0013 .0106 ±.0013 .0106 ±.0010 .0107 ±.0006 .0106 ±.0010 .0104 ±.0011 .0113 ±.0021 .0105 ±.0011 .0104 ±.0011 .0105 ±.0010 4 .0105 ±.0011 .0103 ±.0012 .0107 ±.0011 .0107 ±.0011 .0108 ±.0012 .0103 ±.0012 .0106 ±.0012 .0109 ±.0012 .0108 ±.0011 .0107 ±.0010 .0105 ±.0011 .0116 ±.0005 .0105 ±.0010 .0103 ±.0010 .0103 ±.0012 5 .0105 ±.0012 .0103 ±.0012 .0106 ±.0011 .0108 ±.0013 .0104 ±.0011 .0103 ±.0012 .0105 ±.0012 .0108 ±.0015 .0108 ±.0014 .0105 ±.0010 .0104 ±.0010 .0120 ±.0005 .0105 ±.0012 .0104 ±.0011 .0104 ±.0012 brasilian houses ( × 10 3 ) 1 13.5 ±6.5 13.8 ±6.8 13.5 ±6.5 15.9 ±5.5 14.9 ±7.6 13.8 ±6.8 13.9 ±6.7 13.5 ±6.5 15.9 ±5.5 13.5 ±6.5 13.7 ±6.7 15.0 ±4.3 15.3 ±3.8 13.5 ±6.5 14.2 ±7.0 2 13.6 ±6.4 13.6 ±6.4 13.6 ±6.4 13.8 ±6.6 14.9 ±7.6 13.6 ±6.4 13.5 ±6.9 13.7 ±6.1 13.6 ±6.4 13.6 ±6.3 13.7 ±6.5 16.0 ±5.3 13.8 ±6.3 13.6 ±6.4 14.2 ±7.2 3 13.7 ±6.6 13.9 ±6.2 13.9 ±6.2 13.8 ±6.4 14.8 ±7.6 13.8 ±6.2 13.4 ±6.9 13.6 ±6.7 13.6 ±6.8 13.5 ±6.8 13.9 ±6.1 14.0 ±7.5 13.7 ±6.5 13.7 ±6.6 14.0 ±7.5 4 13.7 ±6.7 13.8 ±6.3 13.7 ±6.6 13.6 ±6.7 14.7 ±7.7 13.6 ±6.5 13.5 ±7.0 13.7 ±6.7 13.7 ±6.6 13.8 ±6.4 13.8 ±6.3 14.5 ±7.8 13.8 ±6.4 13.6 ±6.8 13.9 ±7.5 5 13.7 ±6.8 13.8 ±6.4 13.7 ±6.6 13.8 ±6.4 15.9 ±5.6 13.8 ±6.4 13.9 ±6.5 13.7 ±6.6 13.9 ±6.3 13.7 ±6.6 13.8 ±6.5 13.8 ±6.5 13.8 ±6.4 13.6 ±6.9 13.4 ±7.0 automobile 1 .615 ±.092 .591 ±.156 .545 ±.167 .625 ±.074 1.05 ±.08 .587 ±.163 .598 ±.134 .811 ±.090 .574 ±.140 .674 ±.165 .645 ±.191 .709 ±.069 .712 ±.087 .556 ±.144 .715 ±.142 2 .581 ±.112 .601 ±.151 .549 ±.130 .629 ±.110 .800 ±.142 .601 ±.151 .597 ±.069 .730 ±.019 .572 ±.171 .608 ±.113 .610 ±.139 .732 ±.067 .573 ±.091 .553 ±.122 .630 ±.114 3 .537 ±.144 .631 ±.153 .570 ±.119 .611 ±.112 .647 ±.052 .631 ±.153 .581 ±.080 .587 ±.080 .565 ±.177 .582 ±.117 .560 ±.158 .603 ±.120 .567 ±.067 .559 ±.128 .626 ±.090 4 .523 ±.128 .632 ±.137 .558 ±.150 .572 ±.125 .618 ±.057 .632 ±.137 .587 ±.072 .546 ±.091 .547 ±.106 .556 ±.128 .547 ±.160 .562 ±.094 .570 ±.095 .553 ±.110 .627 ±.081 5 .533 ±.143 .565 ±.123 .554 ±.130 .560 ±.125 .581 ±.126 .565 ±.123 .547 ±.109 .548 ±.096 .554 ±.107 .555 ±.131 .530 ±.127 .571 ±.113 .558 ±.108 .558 ±.102 .628 ±.052 pva_revenue prediction_kddcup98 1 .600 ±.001 .599 ±.002 .600 ±.002 .599 ±.001 .600 ±.002 .599 ±.002 .599 ±.002 .600 ±.002 .600 ±.002 .600 ±.002 .599 ±.002 .599 ±.002 .599 ±.002 .600 ±.002 .599 ±.002 2 .599 ±.002 .599 ±.002 .598 ±.002 .599 ±.001 .599 ±.001 .599 ±.002 .599 ±.002 .599 ±.002 .599 ±.002 .600 ±.002 .599 ±.002 .600 ±.002 .599 ±.002 .599 ±.002 .599 ±.001 3 .599 ±.002 .599 ±.002 .599 ±.001 .599 ±.002 .599 ±.002 .598 ±.002 .600 ±.002 .599 ±.002 .599 ±.002 .601 ±.001 .599 ±.002 .599 ±.002 .599 ±.002 .599 ±.002 .599 ±.003 4 .601 ±.002 .601 ±.002 .600 ±.002 .601 ±.001 .604 ±.002 .601 ±.002 .602 ±.002 .601 ±.003 .602 ±.003 .602 ±.002 .600 ±.002 .602 ±.002 .602 ±.003 .601 ±.002 .602 ±.001 5 .604 ±.002 .604 ±.002 .603 ±.002 .604 ±.002 .605 ±.002 .604 ±.002 .605 ±.002 .604 ±.002 .604 ±.002 .605 ±.002 .604 ±.002 .604 ±.002 .604 ±.002 .604 ±.002 .605 ±.002 superconductivity 1 9.06 ±.11 9.01 ±.07 9.19 ±.04 9.24 ±.04 9.99 ±.06 9.09 ±.06 9.04 ±.03 9.02 ±.01 10.4 ±.5 9.07 ±.01 9.10 ±.06 9.03 ±.01 9.13 ±.07 9.07 ±.10 9.09 ±.04 2 8.88 ±.04 8.85 ±.02 8.90 ±.05 8.87 ±.08 8.95 ±.01 8.86 ±.03 8.86 ±.06 8.93 ±.01 9.09 ±.05 8.88 ±.04 8.90 ±.04 8.92 ±.02 8.87 ±.01 8.83 ±.02 8.92 ±.03 3 8.82 ±.03 8.83 ±.03 8.90 ±.08 8.85 ±.05 8.91 ±.02 8.81 ±.03 8.79 ±.04 8.85 ±.04 9.01 ±.03 8.84 ±.06 8.82 ±.05 8.85 ±.03 8.85 ±.06 8.83 ±.02 8.88 ±.07 4 8.83 ±.03 8.83 ±.05 8.86 ±.02 8.84 ±.01 8.89 ±.01 8.80 ±.01 8.83 ±.03 8.83 ±.03 8.87 ±.02 8.82 ±.03 8.82 ±.03 8.86 ±.04 8.83 ±.01 8.82 ±.03 8.81 ±.07 5 8.84 ±.04 8.82 ±.05 8.85 ±.01 8.81 ±.03 8.83 ±.03 8.80 ±.02 8.83 ±.01 8.81 ±.04 8.84 ±.04 8.81 ±.01 8.79 ±.01 8.82 ±.02 8.82 ±.03 8.81 ±.03 8.81 ±.03 AUC (↑) in_vehicle_coupon recommendation 1 .748 ±.012 .719 ±.006 .697 ±.008 .720 ±.013 .584 ±.015 .720 ±.008 .740 ±.011 .729 ±.010 .722 ±.024 .716 ±.017 .748 ±.012 .608 ±.039 .738 ±.018 .750 ±.008 .745 ±.008 2 .800 ±.007 .726 ±.006 .721 ±.011 .764 ±.012 .631 ±.005 .726 ±.006 .802 ±.026 .768 ±.011 .760 ±.013 .740 ±.011 .792 ±.012 .666 ±.022 .816 ±.015 .804 ±.004 .766 ±.021 3 .815 ±.003 .755 ±.017 .738 ±.006 .811 ±.003 .677 ±.002 .756 ±.017 .825 ±.007 .818 ±.004 .801 ±.012 .760 ±.011 .813 ±.006 .742 ±.056 .833 ±.008 .810 ±.004 .798 ±.038 4 .839 ±.006 .783 ±.008 .811 ±.018 .830 ±.003 .737 ±.002 .784 ±.014 .834 ±.010 .829 ±.005 .832 ±.004 .796 ±.002 .827 ±.011 .815 ±.005 .845 ±.001 .843 ±.004 .820 ±.027 5 .843 ±.003 .832 ±.004 .832 ±.004 .838 ±.004 .815 ±.006 .832 ±.005 .844 ±.002 .837 ±.009 .839 ±.006 .835 ±.004 .843 ±.004 .837 ±.009 .844 ±.002 .844 ±.003 .836 ±.009 marketing campaign 1 .836 ±.043 .826 ±.045 .796 ±.018 .795 ±.028 .710 ±.022 .826 ±.045 .801 ±.026 .816 ±.012 .788 ±.017 .827 ±.044 .763 ±.003 .775 ±.035 .838 ±.036 .847 ±.023 .807 ±.056 2 .872 ±.029 .884 ±.032 .888 ±.022 .840 ±.026 .836 ±.015 .890 ±.034 .875 ±.013 .864 ±.010 .853 ±.018 .867 ±.039 .824 ±.043 .819 ±.008 .884 ±.024 .868 ±.031 .866 ±.012 3 .893 ±.014 .906 ±.023 .893 ±.026 .885 ±.008 .878 ±.018 .907 ±.022 .902 ±.022 .886 ±.003 .881 ±.016 .890 ±.023 .878 ±.029 .850 ±.026 .902 ±.015 .884 ±.020 .905 ±.021 4 .911 ±.018 .910 ±.024 .906 ±.020 .897 ±.009 .880 ±.017 .914 ±.024 .914 ±.019 .895 ±.005 .909 ±.015 .902 ±.024 .898 ±.030 .891 ±.040 .916 ±.014 .904 ±.012 .917 ±.021 5 .920 ±.017 .917 ±.022 .921 ±.016 .904 ±.017 .903 ±.030 .918 ±.020 .919 ±.022 .916 ±.010 .922 ±.017 .913 ±.018 .911 ±.017 .907 ±.035 .922 ±.018 .918 ±.018 .923 ±.020 polish_companies bankruptcy 1 .968 ±.005 .851 ±.004 .885 ±.011 .907 ±.021 .876 ±.031 .860 ±.012 .966 ±.019 .962 ±.007 .924 ±.019 .932 ±.018 .874 ±.016 .870 ±.013 .891 ±.026 .964 ±.004 .957 ±.012 2 .975 ±.006 .896 ±.031 .923 ±.021 .934 ±.024 .924 ±.017 .885 ±.024 .975 ±.013 .970 ±.002 .944 ±.017 .963 ±.018 .965 ±.023 .935 ±.039 .939 ±.035 .974 ±.009 .966 ±.017 3 .979 ±.008 .932 ±.030 .924 ±.018 .966 ±.005 .929 ±.011 .922 ±.036 .976 ±.014 .980 ±.006 .962 ±.006 .982 ±.009 .976 ±.013 .943 ±.039 .959 ±.033 .979 ±.009 .961 ±.016 4 .983 ±.008 .951 ±.015 .957 ±.030 .964 ±.007 .933 ±.008 .951 ±.015 .982 ±.008 .984 ±.006 .967 ±.003 .985 ±.009 .980 ±.011 .981 ±.006 .971 ±.035 .981 ±.010 .968 ±.017 5 .983 ±.008 .969 ±.020 .977 ±.013 .974 ±.004 .942 ±.007 .971 ±.017 .984 ±.008 .985 ±.007 .977 ±.005 .986 ±.009 .983 ±.010 .977 ±.008 .985 ±.008 .984 ±.008 .984 ±.011 colon_tumor 1 .713 ±.018 .862 ±.069 .876 ±.108 .864 ±.061 .675 ±.228 .820 ±.101 .644 ±.201 .856 ±.117 .871 ±.047 .844 ±.047 .911 ±.047 .577 ±.183 .730 ±.217 .820 ±.138 .642 ±.156 2 .841 ±.080 .888 ±.081 .918 ±.062 .901 ±.065 .851 ±.119 .868 ±.048 .850 ±.136 .789 ±.083 .901 ±.047 .908 ±.046 .895 ±.061 .773 ±.213 .753 ±.212 .918 ±.036 .768 ±.022 3 .834 ±.095 .900 ±.059 .908 ±.062 .890 ±.066 .866 ±.072 .885 ±.044 .798 ±.145 .830 ±.111 .905 ±.079 .895 ±.061 .895 ±.059 .835 ±.072 .872 ±.056 .864 ±.071 .880 ±.036 4 .883 ±.066 .916 ±.066 .894 ±.068 .898 ±.081 .875 ±.099 .902 ±.019 .914 ±.095 .877 ±.097 .891 ±.073 .898 ±.053 .905 ±.069 .889 ±.083 .897 ±.081 .904 ±.076 .871 ±.047 5 .892 ±.067 .898 ±.044 .906 ±.072 .882 ±.077 .909 ±.081 .887 ±.062 .891 ±.073 .901 ±.091 .891 ±.074 .891 ±.065 .898 ±.063 .878 ±.098 .898 ±.079 .898 ±.071 .891 ±.065 mechanisms of_action 1 .940 ±.008 .942 ±.006 .937 ±.012 .941 ±.008 .935 ±.010 .946 ±.012 .924 ±.020 .940 ±.007 .935 ±.011 .943 ±.009 .897 ±.053 .924 ±.009 .940 ±.007 .937 ±.011 .936 ±.008 2 .946 ±.009 .946 ±.008 .940 ±.010 .944 ±.014 .947 ±.007 .946 ±.007 .936 ±.010 .941 ±.010 .942 ±.011 .941 ±.013 .949 ±.004 .939 ±.006 .938 ±.008 .945 ±.009 .935 ±.013 3 .950 ±.011 .949 ±.015 .948 ±.012 .944 ±.009 .949 ±.009 .945 ±.014 .944 ±.009 .950 ±.010 .951 ±.010 .947 ±.009 .945 ±.008 .949 ±.006 .951 ±.006 .947 ±.012 .954 ±.005 4 .948 ±.008 .951 ±.010 .954 ±.007 .959 ±.010 .955 ±.008 .950 ±.010 .948 ±.003 .954 ±.008 .955 ±.009 .948 ±.008 .949 ±.009 .948 ±.007 .950 ±.004 .951 ±.014 .954 ±.005 5 .952 ±.003 .951 ±.008 .955 ±.006 .958 ±.010 .955 ±.005 .952 ±.007 .950 ±.003 .953 ±.006 .956 ±.008 .953 ±.010 .956 ±.005 .953 ±.007 .955 ±.008 .954 ±.010 .956 ±.004 santander_customer transaction_prediction 1 .628 ±.006 .628 ±.005 .630 ±.002 .630 ±.002 .501 ±.001 .628 ±.005 .592 ±.018 .631 ±.002 .504 ±.002 .630 ±.002 .501 ±.003 .510 ±.008 .530 ±.005 .630 ±.002 .575 ±.009 2 .697 ±.003 .700 ±.002 .703 ±.003 .701 ±.004 .499 ±.001 .700 ±.002 .618 ±.021 .700 ±.004 .503 ±.008 .701 ±.004 .502 ±.003 .574 ±.028 .577 ±.031 .699 ±.000 .600 ±.018 3 .783 ±.004 .786 ±.004 .787 ±.004 .781 ±.004 .505 ±.004 .786 ±.004 .633 ±.032 .784 ±.002 .503 ±.006 .787 ±.003 .514 ±.007 .601 ±.033 .596 ±.018 .786 ±.001 .625 ±.020 4 .856 ±.004 .863 ±.003 .863 ±.002 .858 ±.004 .520 ±.003 .863 ±.003 .650 ±.028 .854 ±.003 .537 ±.004 .857 ±.003 .529 ±.012 .646 ±.011 .632 ±.015 .861 ±.003 .670 ±.016 5 .889 ±.002 .887 ±.003 .886 ±.003 .888 ±.002 .530 ±.007 .887 ±.003 .767 ±.014 .880 ±.003 .677 ±.004 .875 ±.002 .570 ±.022 .760 ±.022 .753 ±.011 .889 ±.002 .787 ±.005 73 (continued from previous page) DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCO MarkovBlanket MImRMR(R)ReliefFRandomSBERFImportanceSFS nci_prostate cancer 1 .950 ±.017 .942 ±.019 .943 ±.014 .934 ±.017 .654 ±.034 .924 ±.046 .772 ±.048 .922 ±.052 .936 ±.017 .964 ±.004 .915 ±.017 .816 ±.071 .760 ±.092 .947 ±.033 .819 ±.060 2 .981 ±.014 .966 ±.020 .964 ±.018 .979 ±.012 .761 ±.094 .989 ±.005 .936 ±.029 .991 ±.007 .978 ±.011 .981 ±.011 .979 ±.018 .945 ±.026 .937 ±.019 .976 ±.021 .947 ±.027 3 .992 ±.009 .990 ±.006 .991 ±.006 .989 ±.009 .918 ±.023 .992 ±.005 .983 ±.003 .996 ±.001 .993 ±.006 .994 ±.003 .988 ±.008 .984 ±.004 .988 ±.008 .994 ±.003 .988 ±.009 4 .998 ±.001 .995 ±.004 .993 ±.004 .993 ±.005 .970 ±.014 .995 ±.005 .991 ±.009 .996 ±.001 .994 ±.004 .992 ±.002 .988 ±.010 .993 ±.004 .995 ±.001 .996 ±.003 .995 ±.001 5 .985 ±.005 .991 ±.006 .996 ±.006 .992 ±.002 .971 ±.006 .989 ±.004 .984 ±.001 .992 ±.002 .990 ±.005 .987 ±.003 .993 ±.006 .982 ±.003 .983 ±.004 .992 ±.002 .983 ±.005 bank_customer churn 1 .751 ±.011 .773 ±.013 .773 ±.013 .821 ±.003 .594 ±.006 .773 ±.013 .821 ±.003 .821 ±.003 .821 ±.003 .821 ±.003 .749 ±.062 .505 ±.010 .821 ±.003 .752 ±.009 .821 ±.003 2 .766 ±.007 .793 ±.007 .793 ±.007 .854 ±.010 .767 ±.005 .793 ±.007 .852 ±.010 .855 ±.010 .841 ±.012 .839 ±.008 .821 ±.003 .689 ±.069 .837 ±.009 .767 ±.007 .846 ±.004 3 .837 ±.003 .806 ±.006 .841 ±.024 .853 ±.010 .784 ±.008 .806 ±.006 .860 ±.015 .853 ±.010 .863 ±.014 .838 ±.007 .836 ±.004 .717 ±.093 .847 ±.004 .837 ±.003 .851 ±.005 4 .850 ±.001 .849 ±.030 .869 ±.008 .855 ±.011 .818 ±.009 .849 ±.030 .866 ±.006 .855 ±.011 .866 ±.014 .852 ±.004 .849 ±.007 .809 ±.047 .870 ±.008 .848 ±.004 .865 ±.015 5 .870 ±.008 .848 ±.031 .870 ±.007 .860 ±.013 .818 ±.007 .848 ±.031 .870 ±.007 .865 ±.005 .870 ±.007 .852 ±.003 .859 ±.011 .844 ±.032 .870 ±.008 .868 ±.008 .870 ±.008 hepatitis_survival prediction 1 .814 ±.033 .781 ±.065 .812 ±.033 .833 ±.018 .807 ±.045 .806 ±.030 .848 ±.025 .844 ±.026 .802 ±.030 .850 ±.033 .821 ±.061 .842 ±.029 .796 ±.069 .809 ±.024 .791 ±.046 2 .842 ±.012 .840 ±.052 .854 ±.022 .860 ±.002 .818 ±.046 .840 ±.038 .860 ±.001 .869 ±.008 .848 ±.021 .863 ±.019 .844 ±.014 .831 ±.024 .841 ±.054 .812 ±.024 .827 ±.011 3 .859 ±.013 .851 ±.050 .866 ±.017 .861 ±.013 .838 ±.027 .864 ±.026 .853 ±.027 .867 ±.023 .885 ±.026 .858 ±.021 .846 ±.025 .851 ±.008 .869 ±.021 .852 ±.021 .840 ±.021 4 .877 ±.028 .864 ±.037 .883 ±.026 .881 ±.024 .879 ±.023 .873 ±.030 .869 ±.038 .882 ±.017 .888 ±.020 .877 ±.018 .857 ±.002 .861 ±.022 .888 ±.006 .877 ±.027 .869 ±.019 5 .875 ±.030 .880 ±.026 .879 ±.025 .884 ±.026 .880 ±.015 .886 ±.022 .883 ±.022 .886 ±.031 .889 ±.025 .877 ±.020 .890 ±.027 .904 ±.029 .888 ±.003 .889 ±.030 .887 ±.028 customer_satisfaction in_airline 1 .957 ±.000 .920 ±.001 .935 ±.002 .924 ±.000 .940 ±.001 .920 ±.001 .941 ±.002 .961 ±.000 .959 ±.000 .913 ±.000 .956 ±.001 .858 ±.052 .910 ±.105 .959 ±.000 .971 ±.001 2 .979 ±.000 .977 ±.001 .957 ±.001 .976 ±.000 .979 ±.000 .977 ±.001 .977 ±.000 .978 ±.000 .978 ±.000 .973 ±.000 .977 ±.005 .890 ±.020 .985 ±.002 .976 ±.001 .985 ±.001 3 .990 ±.000 .990 ±.000 .982 ±.008 .987 ±.000 .989 ±.001 .990 ±.000 .991 ±.001 .988 ±.000 .988 ±.000 .985 ±.000 .990 ±.000 .979 ±.010 .992 ±.001 .987 ±.001 .992 ±.001 4 .993 ±.000 .992 ±.000 .992 ±.001 .992 ±.000 .993 ±.000 .992 ±.000 .994 ±.000 .991 ±.000 .992 ±.000 .991 ±.000 .993 ±.000 .991 ±.002 .994 ±.000 .992 ±.000 .994 ±.000 5 .995 ±.000 .994 ±.000 .994 ±.000 .993 ±.000 .994 ±.000 .994 ±.000 .995 ±.000 .994 ±.000 .995 ±.000 .994 ±.000 .995 ±.000 .994 ±.001 .995 ±.000 .995 ±.000 .995 ±.000 heloc 1 .778 ±.012 .769 ±.006 .777 ±.010 .772 ±.012 .665 ±.003 .772 ±.003 .761 ±.050 .789 ±.009 .773 ±.010 .777 ±.017 .736 ±.013 .708 ±.081 .787 ±.010 .777 ±.011 .757 ±.023 2 .781 ±.011 .783 ±.006 .788 ±.012 .786 ±.008 .767 ±.015 .785 ±.006 .788 ±.018 .794 ±.009 .790 ±.009 .796 ±.010 .795 ±.007 .757 ±.015 .795 ±.011 .784 ±.010 .794 ±.008 3 .798 ±.008 .789 ±.007 .798 ±.007 .798 ±.006 .774 ±.011 .789 ±.008 .792 ±.018 .798 ±.005 .799 ±.006 .798 ±.008 .799 ±.008 .776 ±.010 .798 ±.009 .798 ±.008 .796 ±.009 4 .801 ±.008 .797 ±.005 .799 ±.007 .800 ±.007 .783 ±.008 .798 ±.007 .798 ±.010 .801 ±.007 .800 ±.006 .799 ±.008 .801 ±.008 .790 ±.010 .800 ±.009 .801 ±.008 .798 ±.008 5 .802 ±.008 .801 ±.008 .801 ±.008 .803 ±.007 .798 ±.012 .800 ±.007 .800 ±.008 .802 ±.008 .801 ±.008 .800 ±.007 .801 ±.009 .799 ±.006 .800 ±.009 .802 ±.009 .800 ±.009 hiva agnostic 1 .580 ±.032 .666 ±.045 .570 ±.036 .532 ±.057 .547 ±.023 .589 ±.045 .520 ±.096 .519 ±.055 .607 ±.071 .532 ±.057 .641 ±.043 .557 ±.061 .563 ±.028 .595 ±.062 .657 ±.071 2 .696 ±.025 .715 ±.044 .632 ±.085 .638 ±.028 .572 ±.037 .749 ±.012 .623 ±.046 .607 ±.047 .652 ±.130 .638 ±.028 .678 ±.070 .624 ±.029 .595 ±.028 .706 ±.019 .677 ±.019 3 .733 ±.041 .772 ±.025 .668 ±.128 .732 ±.021 .625 ±.055 .762 ±.004 .704 ±.040 .732 ±.056 .703 ±.118 .732 ±.021 .756 ±.025 .734 ±.056 .730 ±.023 .767 ±.021 .767 ±.004 4 .758 ±.015 .792 ±.037 .790 ±.033 .754 ±.009 .719 ±.040 .774 ±.031 .752 ±.041 .772 ±.031 .774 ±.048 .754 ±.009 .787 ±.023 .768 ±.032 .770 ±.025 .794 ±.023 .774 ±.018 5 .775 ±.027 .786 ±.028 .799 ±.022 .774 ±.017 .769 ±.012 .786 ±.029 .790 ±.035 .767 ±.021 .777 ±.037 .774 ±.017 .779 ±.029 .789 ±.024 .790 ±.023 .785 ±.040 .796 ±.035 bank marketing 1 .672 ±.020 .707 ±.014 .707 ±.014 .629 ±.056 .625 ±.008 .707 ±.014 .702 ±.007 .684 ±.023 .676 ±.012 .671 ±.007 .676 ±.008 .676 ±.020 .706 ±.009 .660 ±.001 .690 ±.036 2 .717 ±.007 .716 ±.015 .722 ±.012 .712 ±.027 .652 ±.007 .716 ±.015 .734 ±.014 .709 ±.027 .728 ±.014 .699 ±.008 .702 ±.007 .693 ±.031 .730 ±.004 .716 ±.012 .734 ±.011 3 .729 ±.010 .713 ±.017 .737 ±.016 .733 ±.009 .734 ±.014 .713 ±.017 .747 ±.014 .734 ±.009 .732 ±.022 .735 ±.005 .717 ±.008 .724 ±.025 .748 ±.014 .729 ±.010 .737 ±.007 4 .746 ±.016 .745 ±.006 .745 ±.009 .745 ±.011 .741 ±.013 .741 ±.009 .749 ±.004 .744 ±.013 .760 ±.008 .752 ±.014 .746 ±.009 .738 ±.004 .753 ±.009 .733 ±.014 .748 ±.011 5 .759 ±.010 .760 ±.011 .756 ±.012 .728 ±.032 .755 ±.011 .762 ±.009 .761 ±.010 .755 ±.019 .761 ±.009 .761 ±.009 .756 ±.008 .750 ±.008 .760 ±.006 .762 ±.008 .757 ±.012 leukemia allaml 1 .898 ±.014 .992 ±.014 .995 ±.009 1.00 ±.00 .665 ±.178 .984 ±.027 .672 ±.203 .995 ±.009 .995 ±.009 .973 ±.035 .990 ±.018 .760 ±.273 .615 ±.256 .998 ±.004 .582 ±.080 2 .942 ±.045 .978 ±.027 .985 ±.020 .997 ±.005 .867 ±.044 1.00 ±.00 .825 ±.114 .960 ±.050 .995 ±.009 .995 ±.009 .995 ±.009 .736 ±.102 .899 ±.081 1.00 ±.00 .851 ±.111 3 .982 ±.020 .995 ±.009 .990 ±.017 .998 ±.004 .952 ±.043 .998 ±.004 .960 ±.033 .980 ±.034 .995 ±.009 .995 ±.009 1.00 ±.00 .938 ±.025 .970 ±.034 .998 ±.004 .977 ±.008 4 .975 ±.030 1.00 ±.00 1.00 ±.00 .998 ±.004 .982 ±.016 .995 ±.009 .978 ±.025 .990 ±.017 .998 ±.004 1.00 ±.00 1.00 ±.00 .975 ±.043 .978 ±.038 1.00 ±.00 .997 ±.005 5 .983 ±.024 .993 ±.013 .995 ±.009 .995 ±.009 .990 ±.011 .993 ±.013 .990 ±.017 .990 ±.011 .995 ±.009 .995 ±.009 .993 ±.013 .983 ±.024 .983 ±.024 .993 ±.013 .973 ±.041 heart_disease cleveland 1 .836 ±.024 .836 ±.016 .815 ±.013 .845 ±.072 .767 ±.028 .847 ±.045 .853 ±.087 .821 ±.033 .850 ±.040 .833 ±.051 .853 ±.031 .834 ±.021 .844 ±.058 .829 ±.042 .821 ±.052 2 .856 ±.044 .877 ±.003 .879 ±.005 .900 ±.032 .809 ±.008 .858 ±.046 .860 ±.062 .853 ±.046 .882 ±.026 .874 ±.010 .883 ±.010 .855 ±.015 .837 ±.070 .893 ±.038 .852 ±.026 3 .877 ±.030 .902 ±.014 .888 ±.012 .905 ±.027 .866 ±.006 .881 ±.032 .869 ±.046 .885 ±.022 .897 ±.034 .885 ±.010 .901 ±.010 .828 ±.035 .880 ±.052 .897 ±.038 .890 ±.037 4 .886 ±.024 .903 ±.016 .909 ±.013 .907 ±.032 .882 ±.031 .903 ±.016 .878 ±.056 .905 ±.034 .901 ±.034 .905 ±.018 .906 ±.011 .917 ±.026 .875 ±.053 .898 ±.034 .902 ±.036 5 .902 ±.027 .910 ±.014 .913 ±.020 .899 ±.035 .900 ±.025 .903 ±.038 .897 ±.046 .908 ±.038 .908 ±.037 .906 ±.035 .909 ±.011 .896 ±.041 .898 ±.051 .908 ±.035 .904 ±.033 clock_protein period 1 .550 ±.033 .646 ±.151 .626 ±.098 .524 ±.143 .690 ±.053 .646 ±.151 .575 ±.098 .540 ±.046 .503 ±.124 .530 ±.084 .571 ±.124 .661 ±.125 .619 ±.078 .564 ±.121 .594 ±.053 2 .571 ±.075 .631 ±.155 .640 ±.111 .527 ±.133 .600 ±.096 .631 ±.155 .573 ±.076 .533 ±.066 .538 ±.115 .586 ±.175 .577 ±.165 .568 ±.146 .519 ±.078 .550 ±.170 .651 ±.137 3 .573 ±.090 .573 ±.143 .623 ±.204 .536 ±.175 .630 ±.111 .573 ±.143 .586 ±.123 .543 ±.159 .499 ±.119 .559 ±.146 .510 ±.138 .642 ±.176 .654 ±.140 .570 ±.167 .624 ±.144 4 .559 ±.094 .557 ±.090 .556 ±.216 .533 ±.163 .631 ±.111 .557 ±.090 .543 ±.114 .526 ±.132 .524 ±.146 .487 ±.117 .526 ±.186 .610 ±.113 .577 ±.124 .561 ±.154 .559 ±.143 5 .485 ±.068 .527 ±.103 .529 ±.127 .517 ±.160 .594 ±.032 .527 ±.103 .524 ±.121 .538 ±.124 .552 ±.138 .515 ±.124 .522 ±.115 .556 ±.122 .573 ±.162 .549 ±.125 .536 ±.098 churn 1 .827 ±.024 .885 ±.008 .876 ±.010 .813 ±.007 .704 ±.011 .788 ±.182 .708 ±.015 .814 ±.008 .876 ±.010 .866 ±.040 .803 ±.007 .687 ±.120 .891 ±.003 .876 ±.011 .891 ±.003 2 .902 ±.019 .907 ±.003 .908 ±.003 .843 ±.013 .755 ±.067 .860 ±.089 .823 ±.081 .863 ±.008 .891 ±.007 .898 ±.012 .815 ±.010 .722 ±.109 .922 ±.008 .903 ±.003 .923 ±.006 3 .935 ±.006 .924 ±.007 .921 ±.004 .885 ±.009 .858 ±.009 .933 ±.007 .852 ±.064 .865 ±.006 .907 ±.009 .927 ±.003 .909 ±.002 .764 ±.113 .935 ±.006 .924 ±.005 .928 ±.004 4 .935 ±.006 .932 ±.007 .933 ±.007 .888 ±.008 .858 ±.008 .938 ±.008 .895 ±.070 .874 ±.010 .925 ±.003 .936 ±.007 .927 ±.003 .872 ±.018 .936 ±.006 .934 ±.007 .934 ±.006 5 .933 ±.010 .933 ±.007 .935 ±.008 .889 ±.008 .931 ±.009 .935 ±.008 .933 ±.011 .876 ±.010 .931 ±.011 .934 ±.011 .927 ±.004 .900 ±.049 .933 ±.008 .934 ±.009 .932 ±.011 aps_failure 1 .981 ±.005 .955 ±.046 .979 ±.004 .963 ±.010 .930 ±.013 .977 ±.002 .949 ±.028 .981 ±.006 .983 ±.003 .973 ±.007 .945 ±.004 .968 ±.018 .969 ±.005 .985 ±.004 .979 ±.002 2 .987 ±.003 .984 ±.009 .981 ±.003 .977 ±.004 .964 ±.003 .984 ±.004 .980 ±.007 .986 ±.005 .988 ±.002 .983 ±.005 .974 ±.003 .982 ±.007 .982 ±.009 .989 ±.002 .988 ±.004 3 .992 ±.002 .992 ±.002 .990 ±.003 .980 ±.003 .984 ±.004 .991 ±.001 .990 ±.003 .992 ±.002 .991 ±.003 .988 ±.003 .989 ±.002 .990 ±.003 .990 ±.002 .992 ±.002 .990 ±.003 4 .993 ±.002 .994 ±.002 .993 ±.003 .992 ±.003 .991 ±.003 .993 ±.002 .993 ±.001 .994 ±.002 .993 ±.002 .990 ±.003 .992 ±.002 .993 ±.003 .993 ±.002 .994 ±.002 .993 ±.003 5 .994 ±.002 .995 ±.002 .994 ±.002 .994 ±.002 .994 ±.002 .994 ±.002 .994 ±.002 .994 ±.002 .994 ±.003 .994 ±.002 .994 ±.002 .994 ±.002 .994 ±.002 .994 ±.002 .995 ±.002 labor negotiations 1 .853 ±.078 .861 ±.111 .871 ±.118 .877 ±.107 .927 ±.076 .764 ±.165 .812 ±.122 .924 ±.067 .893 ±.117 .879 ±.105 .937 ±.090 .796 ±.158 .871 ±.154 .893 ±.117 .830 ±.149 2 .913 ±.069 .914 ±.076 .894 ±.107 .914 ±.075 .952 ±.063 .900 ±.103 .893 ±.093 .918 ±.078 .935 ±.072 .927 ±.078 .927 ±.071 .829 ±.275 .902 ±.148 .944 ±.067 .868 ±.084 3 .934 ±.057 .947 ±.049 .944 ±.067 .948 ±.069 .968 ±.036 .929 ±.071 .919 ±.099 .956 ±.065 .968 ±.036 .944 ±.067 .959 ±.042 .937 ±.110 .971 ±.032 .960 ±.059 .914 ±.044 4 .967 ±.029 .955 ±.042 .971 ±.026 .964 ±.062 .980 ±.025 .972 ±.038 .960 ±.069 .968 ±.036 .959 ±.037 .955 ±.039 .955 ±.042 .968 ±.036 .976 ±.024 .984 ±.018 .958 ±.045 5 .967 ±.032 .971 ±.026 .971 ±.026 .963 ±.032 .963 ±.033 .967 ±.029 .951 ±.048 .967 ±.029 .955 ±.040 .951 ±.043 .959 ±.036 .934 ±.060 .971 ±.032 .971 ±.026 .967 ±.032 kdd_cup_09 appetency 1 .652 ±.098 .515 ±.013 .794 ±.021 .758 ±.023 .541 ±.033 .515 ±.013 .812 ±.016 .793 ±.018 .626 ±.005 .744 ±.033 .666 ±.017 .507 ±.041 .491 ±.024 .756 ±.020 .813 ±.017 2 .741 ±.061 .511 ±.022 .800 ±.021 .744 ±.031 .520 ±.050 .511 ±.022 .815 ±.015 .818 ±.019 .643 ±.014 .776 ±.044 .677 ±.015 .494 ±.043 .523 ±.026 .793 ±.018 .805 ±.013 3 .801 ±.046 .560 ±.051 .807 ±.016 .807 ±.014 .533 ±.039 .560 ±.051 .815 ±.014 .819 ±.015 .739 ±.074 .789 ±.037 .810 ±.017 .558 ±.042 .596 ±.032 .800 ±.020 .811 ±.027 4 .828 ±.016 .604 ±.066 .823 ±.015 .812 ±.030 .516 ±.018 .600 ±.072 .821 ±.017 .819 ±.014 .798 ±.015 .826 ±.020 .817 ±.024 .697 ±.073 .689 ±.046 .796 ±.021 .816 ±.015 5 .830 ±.016 .715 ±.116 .830 ±.015 .822 ±.012 .676 ±.018 .715 ±.116 .821 ±.016 .817 ±.012 .797 ±.018 .826 ±.016 .832 ±.015 .758 ±.047 .761 ±.041 .831 ±.015 .824 ±.012 74 (continued from previous page) DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCO MarkovBlanket MImRMR(R)ReliefFRandomSBERFImportanceSFS qsar_oral toxicity 1 .720 ±.016 .692 ±.041 .692 ±.025 .566 ±.056 .562 ±.015 .696 ±.034 .565 ±.033 .555 ±.043 .686 ±.021 .566 ±.056 .564 ±.033 .575 ±.009 .570 ±.010 .693 ±.027 .738 ±.014 2 .775 ±.010 .749 ±.029 .715 ±.033 .622 ±.026 .582 ±.005 .781 ±.016 .642 ±.037 .634 ±.042 .726 ±.023 .622 ±.026 .687 ±.010 .632 ±.052 .659 ±.041 .736 ±.045 .766 ±.008 3 .848 ±.015 .832 ±.030 .796 ±.041 .748 ±.024 .732 ±.010 .845 ±.018 .762 ±.015 .769 ±.033 .816 ±.025 .748 ±.024 .835 ±.020 .769 ±.028 .775 ±.011 .829 ±.036 .822 ±.044 4 .896 ±.021 .878 ±.026 .889 ±.028 .872 ±.019 .863 ±.025 .883 ±.027 .875 ±.027 .878 ±.030 .887 ±.021 .872 ±.019 .903 ±.020 .874 ±.020 .874 ±.009 .895 ±.023 .885 ±.025 5 .905 ±.022 .901 ±.025 .903 ±.027 .900 ±.018 .904 ±.020 .900 ±.026 .899 ±.018 .899 ±.022 .907 ±.018 .900 ±.018 .906 ±.025 .899 ±.026 .897 ±.024 .905 ±.028 .905 ±.020 taiwanese_bankruptcy prediction 1 .944 ±.004 .944 ±.004 .943 ±.003 .946 ±.004 .919 ±.014 .934 ±.016 .923 ±.005 .942 ±.002 .942 ±.004 .918 ±.002 .935 ±.012 .935 ±.011 .935 ±.003 .945 ±.003 .942 ±.003 2 .946 ±.004 .950 ±.008 .946 ±.001 .946 ±.005 .946 ±.007 .945 ±.002 .945 ±.007 .949 ±.003 .945 ±.004 .940 ±.008 .948 ±.006 .945 ±.005 .941 ±.002 .945 ±.004 .944 ±.007 3 .949 ±.007 .950 ±.004 .948 ±.003 .949 ±.004 .947 ±.006 .948 ±.001 .945 ±.007 .949 ±.003 .948 ±.003 .945 ±.003 .948 ±.004 .948 ±.003 .948 ±.004 .946 ±.004 .948 ±.007 4 .949 ±.005 .951 ±.002 .949 ±.002 .949 ±.004 .948 ±.004 .950 ±.003 .947 ±.003 .950 ±.004 .949 ±.004 .948 ±.004 .951 ±.004 .947 ±.004 .948 ±.003 .948 ±.004 .947 ±.006 5 .949 ±.004 .950 ±.004 .949 ±.005 .949 ±.003 .949 ±.004 .948 ±.005 .948 ±.004 .949 ±.003 .948 ±.004 .948 ±.004 .949 ±.004 .948 ±.006 .949 ±.003 .949 ±.004 .948 ±.006 thyroid discordant 1 .982 ±.004 .779 ±.234 .927 ±.033 .983 ±.001 .535 ±.001 .779 ±.234 .980 ±.006 .984 ±.003 .929 ±.023 .930 ±.029 .933 ±.042 .737 ±.206 .981 ±.007 .983 ±.001 .941 ±.057 2 .987 ±.006 .897 ±.094 .948 ±.044 .985 ±.001 .569 ±.007 .897 ±.094 .988 ±.002 .985 ±.003 .924 ±.033 .930 ±.028 .944 ±.036 .760 ±.213 .992 ±.002 .991 ±.002 .937 ±.064 3 .991 ±.001 .949 ±.047 .973 ±.030 .989 ±.004 .581 ±.012 .949 ±.047 .988 ±.004 .985 ±.004 .932 ±.037 .940 ±.026 .947 ±.057 .967 ±.036 .992 ±.003 .992 ±.003 .918 ±.095 4 .992 ±.002 .957 ±.043 .973 ±.036 .991 ±.002 .615 ±.022 .957 ±.043 .988 ±.004 .987 ±.003 .950 ±.039 .956 ±.036 .989 ±.005 .979 ±.015 .993 ±.003 .992 ±.003 .943 ±.055 5 .993 ±.003 .965 ±.046 .991 ±.003 .991 ±.004 .931 ±.054 .965 ±.046 .990 ±.002 .990 ±.002 .970 ±.039 .959 ±.035 .989 ±.006 .988 ±.001 .993 ±.003 .991 ±.004 .970 ±.040 coil_2000 insurance_policies 1 .748 ±.035 .724 ±.061 .759 ±.031 .756 ±.029 .537 ±.002 .671 ±.099 .747 ±.028 .750 ±.036 .753 ±.017 .734 ±.036 .657 ±.030 .712 ±.045 .693 ±.043 .752 ±.034 .766 ±.028 2 .760 ±.033 .752 ±.042 .763 ±.033 .756 ±.030 .574 ±.038 .739 ±.051 .759 ±.036 .756 ±.031 .761 ±.030 .741 ±.036 .662 ±.031 .755 ±.038 .714 ±.046 .756 ±.035 .773 ±.032 3 .771 ±.039 .757 ±.044 .768 ±.035 .759 ±.031 .666 ±.030 .744 ±.047 .763 ±.033 .760 ±.029 .770 ±.027 .753 ±.035 .714 ±.016 .727 ±.014 .718 ±.044 .757 ±.031 .769 ±.033 4 .773 ±.032 .758 ±.049 .776 ±.034 .766 ±.030 .666 ±.033 .751 ±.051 .768 ±.033 .767 ±.023 .773 ±.027 .771 ±.021 .743 ±.028 .776 ±.029 .761 ±.020 .773 ±.032 .774 ±.030 5 .777 ±.032 .772 ±.036 .776 ±.032 .772 ±.033 .675 ±.024 .770 ±.035 .770 ±.038 .774 ±.033 .776 ±.032 .777 ±.029 .743 ±.027 .773 ±.037 .771 ±.036 .775 ±.033 .776 ±.031 prostate cancer 1 .925 ±.068 .964 ±.056 .970 ±.043 .971 ±.050 .577 ±.093 .957 ±.044 .706 ±.095 .954 ±.055 .960 ±.064 .939 ±.060 .969 ±.045 .608 ±.085 .614 ±.088 .949 ±.050 .871 ±.107 2 .938 ±.061 .969 ±.043 .961 ±.053 .965 ±.057 .767 ±.117 .972 ±.048 .735 ±.199 .947 ±.072 .964 ±.062 .972 ±.048 .973 ±.040 .751 ±.114 .764 ±.122 .967 ±.055 .873 ±.046 3 .968 ±.047 .977 ±.037 .972 ±.045 .969 ±.045 .864 ±.011 .964 ±.040 .886 ±.050 .961 ±.048 .970 ±.052 .963 ±.064 .977 ±.034 .897 ±.038 .905 ±.035 .968 ±.039 .918 ±.049 4 .961 ±.036 .978 ±.032 .978 ±.035 .970 ±.046 .917 ±.024 .976 ±.039 .957 ±.051 .965 ±.051 .964 ±.050 .970 ±.052 .968 ±.056 .960 ±.049 .964 ±.026 .971 ±.044 .961 ±.041 5 .905 ±.041 .960 ±.037 .967 ±.046 .946 ±.055 .905 ±.053 .955 ±.033 .905 ±.024 .839 ±.096 .873 ±.023 .938 ±.033 .970 ±.046 .907 ±.052 .891 ±.045 .943 ±.044 .919 ±.052 credit approval 1 .902 ±.019 .918 ±.010 .913 ±.008 .841 ±.008 .665 ±.071 .910 ±.009 .898 ±.013 .850 ±.022 .913 ±.007 .845 ±.012 .886 ±.020 .767 ±.068 .916 ±.004 .915 ±.006 .916 ±.014 2 .922 ±.007 .919 ±.007 .929 ±.015 .859 ±.011 .764 ±.053 .916 ±.006 .925 ±.015 .857 ±.010 .922 ±.008 .870 ±.044 .900 ±.012 .845 ±.115 .923 ±.009 .922 ±.003 .923 ±.008 3 .922 ±.018 .926 ±.002 .929 ±.010 .876 ±.007 .915 ±.002 .928 ±.002 .930 ±.008 .869 ±.002 .923 ±.005 .895 ±.042 .926 ±.009 .867 ±.054 .923 ±.005 .934 ±.009 .918 ±.005 4 .930 ±.006 .926 ±.003 .933 ±.007 .896 ±.029 .922 ±.002 .930 ±.004 .932 ±.010 .876 ±.009 .930 ±.008 .931 ±.011 .928 ±.013 .888 ±.037 .931 ±.005 .932 ±.009 .925 ±.008 5 .932 ±.007 .933 ±.003 .935 ±.005 .934 ±.005 .927 ±.005 .933 ±.004 .929 ±.006 .916 ±.034 .933 ±.009 .936 ±.008 .937 ±.008 .902 ±.037 .932 ±.005 .933 ±.007 .931 ±.002 heart_disease hungary 1 .789 ±.033 .889 ±.017 .877 ±.023 .842 ±.089 .675 ±.149 .890 ±.020 .721 ±.203 .862 ±.025 .838 ±.072 .865 ±.028 .874 ±.037 .730 ±.137 .841 ±.035 .864 ±.037 .845 ±.034 2 .831 ±.068 .896 ±.022 .891 ±.008 .888 ±.020 .884 ±.048 .888 ±.021 .750 ±.185 .879 ±.011 .894 ±.021 .869 ±.007 .891 ±.017 .875 ±.035 .865 ±.052 .859 ±.028 .869 ±.050 3 .845 ±.042 .888 ±.023 .894 ±.011 .877 ±.016 .881 ±.055 .891 ±.017 .855 ±.057 .876 ±.010 .896 ±.020 .881 ±.017 .896 ±.009 .853 ±.035 .888 ±.029 .882 ±.019 .875 ±.050 4 .884 ±.018 .900 ±.017 .892 ±.018 .878 ±.016 .903 ±.020 .892 ±.024 .882 ±.065 .879 ±.017 .901 ±.020 .896 ±.017 .895 ±.004 .888 ±.011 .903 ±.014 .890 ±.014 .906 ±.017 5 .893 ±.008 .898 ±.013 .896 ±.017 .893 ±.014 .903 ±.011 .891 ±.014 .864 ±.065 .886 ±.022 .901 ±.016 .898 ±.020 .893 ±.006 .886 ±.018 .899 ±.018 .898 ±.014 .900 ±.018 give_me some_credit 1 .618 ±.001 .732 ±.001 .728 ±.002 .728 ±.002 .567 ±.004 .732 ±.001 .747 ±.009 .728 ±.002 .728 ±.055 .728 ±.002 .658 ±.009 .701 ±.034 .713 ±.046 .618 ±.001 .728 ±.055 2 .770 ±.002 .806 ±.014 .810 ±.007 .783 ±.002 .683 ±.020 .806 ±.014 .810 ±.014 .786 ±.002 .796 ±.004 .808 ±.010 .689 ±.005 .794 ±.017 .796 ±.024 .696 ±.006 .799 ±.003 3 .778 ±.004 .822 ±.005 .819 ±.004 .789 ±.002 .736 ±.034 .822 ±.005 .823 ±.010 .798 ±.004 .823 ±.002 .813 ±.011 .788 ±.002 .796 ±.038 .823 ±.006 .778 ±.004 .805 ±.009 4 .820 ±.014 .818 ±.005 .833 ±.003 .797 ±.004 .797 ±.028 .818 ±.005 .839 ±.003 .804 ±.004 .835 ±.002 .825 ±.009 .814 ±.015 .823 ±.012 .842 ±.005 .836 ±.010 .832 ±.007 5 .845 ±.003 .841 ±.003 .843 ±.004 .833 ±.008 .838 ±.012 .841 ±.003 .845 ±.004 .829 ±.002 .847 ±.003 .841 ±.003 .842 ±.004 .845 ±.003 .845 ±.003 .845 ±.004 .843 ±.007 clock_protein toxicity 1 .490 ±.092 .535 ±.068 .452 ±.076 .571 ±.090 .479 ±.087 .535 ±.068 .502 ±.031 .447 ±.066 .535 ±.151 .505 ±.107 .466 ±.077 .493 ±.163 .457 ±.017 .509 ±.105 .542 ±.083 2 .498 ±.041 .518 ±.052 .515 ±.043 .550 ±.142 .482 ±.025 .518 ±.052 .480 ±.048 .522 ±.054 .543 ±.169 .538 ±.127 .452 ±.103 .557 ±.115 .553 ±.022 .542 ±.105 .538 ±.075 3 .472 ±.044 .519 ±.119 .509 ±.035 .598 ±.149 .498 ±.051 .519 ±.119 .510 ±.080 .529 ±.072 .522 ±.192 .500 ±.053 .480 ±.100 .488 ±.062 .510 ±.051 .553 ±.125 .552 ±.104 4 .511 ±.087 .532 ±.113 .526 ±.110 .517 ±.147 .501 ±.091 .532 ±.113 .507 ±.077 .564 ±.171 .502 ±.143 .510 ±.109 .531 ±.106 .512 ±.135 .540 ±.149 .543 ±.132 .542 ±.137 5 .506 ±.139 .498 ±.119 .479 ±.122 .508 ±.128 .501 ±.089 .498 ±.119 .527 ±.124 .497 ±.144 .489 ±.130 .524 ±.121 .511 ±.118 .525 ±.107 .498 ±.127 .496 ±.140 .553 ±.102 online_shoppers_purchasing intention_dataset 1 .899 ±.001 .903 ±.006 .894 ±.010 .890 ±.010 .686 ±.013 .903 ±.006 .924 ±.006 .913 ±.010 .903 ±.006 .921 ±.004 .715 ±.036 .697 ±.017 .926 ±.005 .903 ±.006 .883 ±.009 2 .908 ±.006 .933 ±.004 .910 ±.007 .916 ±.022 .695 ±.007 .933 ±.004 .932 ±.008 .935 ±.005 .916 ±.018 .928 ±.004 .867 ±.107 .714 ±.028 .935 ±.005 .908 ±.006 .933 ±.006 3 .936 ±.004 .935 ±.004 .935 ±.006 .935 ±.005 .758 ±.008 .935 ±.004 .933 ±.007 .936 ±.004 .936 ±.004 .929 ±.006 .930 ±.007 .913 ±.006 .936 ±.005 .936 ±.004 .937 ±.006 4 .936 ±.004 .935 ±.005 .936 ±.005 .936 ±.004 .935 ±.005 .935 ±.005 .935 ±.004 .935 ±.005 .937 ±.005 .936 ±.006 .935 ±.006 .930 ±.005 .937 ±.004 .936 ±.005 .936 ±.005 5 .936 ±.004 .936 ±.006 .937 ±.005 .936 ±.004 .936 ±.005 .936 ±.006 .937 ±.005 .937 ±.005 .937 ±.005 .936 ±.006 .936 ±.005 .880 ±.077 .937 ±.005 .937 ±.005 .937 ±.005 credit_card clients_default 1 .760 ±.007 .759 ±.007 .742 ±.004 .742 ±.004 .679 ±.002 .761 ±.008 .764 ±.004 .765 ±.008 .743 ±.005 .751 ±.003 .752 ±.003 .701 ±.027 .762 ±.006 .765 ±.004 .749 ±.009 2 .778 ±.004 .776 ±.009 .778 ±.006 .780 ±.006 .730 ±.003 .773 ±.007 .775 ±.008 .779 ±.005 .771 ±.007 .765 ±.003 .778 ±.002 .747 ±.039 .780 ±.003 .778 ±.005 .771 ±.010 3 .785 ±.006 .781 ±.009 .782 ±.006 .782 ±.006 .760 ±.021 .780 ±.008 .785 ±.004 .787 ±.007 .784 ±.004 .782 ±.005 .787 ±.004 .775 ±.020 .785 ±.005 .784 ±.004 .782 ±.002 4 .788 ±.005 .788 ±.003 .786 ±.005 .787 ±.003 .779 ±.005 .788 ±.002 .788 ±.004 .789 ±.006 .788 ±.005 .784 ±.005 .789 ±.005 .778 ±.018 .789 ±.005 .788 ±.005 .785 ±.003 5 .790 ±.004 .791 ±.003 .792 ±.004 .790 ±.003 .790 ±.004 .791 ±.004 .791 ±.003 .790 ±.005 .790 ±.003 .790 ±.004 .790 ±.004 .780 ±.022 .791 ±.005 .791 ±.004 .788 ±.001 bioresponse 1 .816 ±.007 .762 ±.008 .781 ±.001 .670 ±.014 .506 ±.004 .771 ±.005 .640 ±.052 .784 ±.035 .821 ±.012 .740 ±.019 .761 ±.006 .546 ±.023 .547 ±.023 .799 ±.019 .766 ±.027 2 .849 ±.015 .809 ±.004 .826 ±.013 .725 ±.023 .509 ±.006 .812 ±.009 .711 ±.060 .852 ±.021 .857 ±.010 .775 ±.021 .810 ±.007 .685 ±.082 .677 ±.053 .855 ±.020 .774 ±.013 3 .863 ±.019 .858 ±.012 .860 ±.014 .844 ±.015 .518 ±.001 .852 ±.011 .821 ±.023 .868 ±.013 .868 ±.012 .829 ±.011 .848 ±.006 .790 ±.024 .747 ±.018 .867 ±.013 .825 ±.015 4 .869 ±.013 .870 ±.010 .867 ±.012 .869 ±.016 .519 ±.005 .867 ±.009 .841 ±.011 .871 ±.014 .872 ±.012 .866 ±.017 .856 ±.007 .847 ±.024 .848 ±.026 .871 ±.012 .857 ±.012 5 .872 ±.010 .872 ±.012 .871 ±.012 .872 ±.012 .791 ±.018 .874 ±.010 .869 ±.008 .873 ±.013 .874 ±.011 .869 ±.017 .869 ±.011 .868 ±.010 .865 ±.012 .873 ±.013 .871 ±.011 lymphography 1 .878 ±.036 .848 ±.121 .854 ±.055 .832 ±.016 .630 ±.062 .850 ±.123 .855 ±.137 .826 ±.009 .882 ±.049 .898 ±.036 .861 ±.088 .774 ±.147 .851 ±.114 .877 ±.054 .854 ±.103 2 .938 ±.010 .865 ±.110 .898 ±.058 .888 ±.022 .693 ±.157 .864 ±.109 .919 ±.051 .862 ±.025 .903 ±.056 .885 ±.034 .913 ±.067 .738 ±.220 .913 ±.038 .942 ±.026 .876 ±.069 3 .939 ±.013 .870 ±.120 .904 ±.058 .900 ±.026 .856 ±.120 .868 ±.115 .896 ±.100 .892 ±.021 .923 ±.040 .889 ±.025 .911 ±.056 .866 ±.034 .915 ±.042 .938 ±.026 .870 ±.103 4 .922 ±.021 .898 ±.056 .923 ±.030 .905 ±.025 .903 ±.083 .896 ±.054 .897 ±.068 .913 ±.023 .908 ±.032 .882 ±.038 .926 ±.038 .874 ±.046 .920 ±.047 .929 ±.026 .874 ±.100 5 .920 ±.037 .904 ±.056 .928 ±.038 .902 ±.033 .905 ±.085 .901 ±.058 .899 ±.070 .906 ±.041 .905 ±.031 .904 ±.069 .933 ±.030 .922 ±.055 .903 ±.069 .927 ±.046 .889 ±.101 nci_ovarian cancer 1 .994 ±.008 .996 ±.004 .990 ±.010 .993 ±.012 .700 ±.124 1.00 ±.00 .859 ±.050 .983 ±.029 .990 ±.010 .999 ±.001 .990 ±.010 .819 ±.176 .817 ±.094 .997 ±.006 .995 ±.002 2 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 .948 ±.075 1.00 ±.00 .991 ±.009 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 .993 ±.003 .995 ±.005 1.00 ±.00 1.00 ±.00 3 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 .976 ±.032 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 4 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 .999 ±.002 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 5 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 .998 ±.001 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 1.00 ±.00 75 (continued from previous page) DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCO MarkovBlanket MImRMR(R)ReliefFRandomSBERFImportanceSFS bad_customer detection 1 .609 ±.022 .622 ±.041 .646 ±.037 .724 ±.020 .658 ±.045 .622 ±.041 .665 ±.048 .658 ±.057 .694 ±.034 .683 ±.025 .620 ±.032 .644 ±.041 .698 ±.029 .612 ±.032 .715 ±.019 2 .706 ±.018 .634 ±.031 .675 ±.020 .730 ±.020 .670 ±.047 .634 ±.031 .676 ±.058 .703 ±.031 .734 ±.019 .710 ±.024 .632 ±.022 .590 ±.035 .713 ±.024 .704 ±.020 .728 ±.014 3 .728 ±.024 .666 ±.001 .671 ±.019 .734 ±.018 .668 ±.042 .666 ±.001 .718 ±.026 .737 ±.016 .739 ±.008 .722 ±.034 .632 ±.020 .707 ±.049 .720 ±.029 .719 ±.015 .729 ±.006 4 .738 ±.017 .682 ±.024 .685 ±.024 .734 ±.014 .690 ±.026 .682 ±.024 .724 ±.021 .737 ±.016 .738 ±.007 .737 ±.017 .668 ±.008 .699 ±.021 .728 ±.017 .736 ±.016 .731 ±.005 5 .734 ±.017 .729 ±.025 .728 ±.022 .749 ±.014 .703 ±.039 .729 ±.025 .738 ±.021 .741 ±.015 .739 ±.008 .738 ±.022 .687 ±.023 .732 ±.027 .735 ±.022 .747 ±.012 .737 ±.021 hazelnut_spread contaminant_detection_10GHz 1 .940 ±.018 .933 ±.016 .933 ±.010 .927 ±.012 .911 ±.011 .930 ±.018 .945 ±.022 .932 ±.013 .928 ±.013 .927 ±.018 .942 ±.008 .892 ±.007 .939 ±.007 .940 ±.019 .934 ±.007 2 .975 ±.012 .967 ±.010 .967 ±.006 .969 ±.008 .951 ±.010 .967 ±.010 .979 ±.013 .969 ±.012 .968 ±.011 .967 ±.011 .975 ±.009 .953 ±.025 .980 ±.008 .969 ±.012 .973 ±.015 3 .990 ±.006 .980 ±.005 .986 ±.006 .982 ±.008 .977 ±.009 .981 ±.005 .987 ±.010 .988 ±.008 .981 ±.007 .984 ±.012 .988 ±.008 .980 ±.011 .989 ±.007 .984 ±.007 .990 ±.008 4 .992 ±.006 .988 ±.006 .991 ±.006 .990 ±.007 .984 ±.009 .988 ±.006 .992 ±.006 .992 ±.006 .989 ±.008 .991 ±.006 .993 ±.006 .990 ±.007 .992 ±.006 .993 ±.005 .991 ±.008 5 .993 ±.006 .994 ±.006 .992 ±.006 .993 ±.005 .991 ±.006 .994 ±.006 .994 ±.006 .993 ±.006 .993 ±.005 .994 ±.006 .994 ±.005 .992 ±.005 .994 ±.005 .994 ±.005 .994 ±.006 drug_induced autoimmunity_prediction 1 .670 ±.084 .642 ±.032 .618 ±.070 .639 ±.039 .606 ±.069 .658 ±.063 .657 ±.051 .712 ±.012 .670 ±.035 .695 ±.019 .597 ±.057 .651 ±.081 .612 ±.056 .697 ±.033 .682 ±.030 2 .728 ±.054 .716 ±.031 .635 ±.011 .731 ±.016 .737 ±.011 .706 ±.049 .738 ±.033 .764 ±.044 .709 ±.029 .714 ±.011 .653 ±.003 .732 ±.033 .640 ±.068 .737 ±.010 .736 ±.045 3 .806 ±.022 .787 ±.008 .792 ±.020 .802 ±.022 .748 ±.008 .781 ±.031 .817 ±.023 .809 ±.031 .774 ±.036 .768 ±.059 .783 ±.043 .783 ±.058 .781 ±.039 .815 ±.019 .756 ±.048 4 .832 ±.014 .820 ±.018 .829 ±.010 .838 ±.006 .820 ±.017 .837 ±.010 .825 ±.021 .843 ±.029 .842 ±.016 .803 ±.016 .821 ±.016 .798 ±.049 .816 ±.039 .821 ±.008 .821 ±.038 5 .845 ±.014 .855 ±.017 .858 ±.004 .847 ±.006 .827 ±.022 .852 ±.012 .843 ±.025 .841 ±.009 .843 ±.017 .818 ±.008 .850 ±.010 .849 ±.006 .846 ±.011 .844 ±.004 .852 ±.017 heart_disease switzerland 1 .339 ±.164 .452 ±.164 .882 ±.090 .609 ±.079 .680 ±.047 .452 ±.164 .507 ±.249 .703 ±.171 .655 ±.168 .591 ±.087 .870 ±.093 .501 ±.188 .454 ±.121 .386 ±.214 .433 ±.088 2 .623 ±.246 .441 ±.022 .785 ±.153 .610 ±.016 .743 ±.158 .441 ±.022 .629 ±.112 .683 ±.215 .866 ±.083 .690 ±.041 .839 ±.146 .599 ±.097 .613 ±.274 .680 ±.145 .397 ±.102 3 .730 ±.178 .448 ±.168 .763 ±.183 .505 ±.104 .578 ±.032 .448 ±.168 .657 ±.098 .543 ±.065 .828 ±.118 .803 ±.156 .769 ±.169 .607 ±.215 .661 ±.300 .711 ±.164 .631 ±.282 4 .752 ±.171 .569 ±.118 .737 ±.209 .584 ±.117 .578 ±.217 .569 ±.118 .743 ±.186 .565 ±.046 .775 ±.164 .796 ±.138 .730 ±.181 .698 ±.215 .762 ±.153 .743 ±.180 .755 ±.168 5 .764 ±.160 .697 ±.213 .731 ±.190 .654 ±.122 .669 ±.096 .697 ±.213 .753 ±.171 .673 ±.239 .768 ±.191 .784 ±.172 .723 ±.200 .739 ±.174 .775 ±.152 .761 ±.165 .743 ±.183 hr analytics 1 .721 ±.002 .785 ±.007 .785 ±.007 .758 ±.038 .637 ±.005 .785 ±.007 .690 ±.002 .785 ±.007 .715 ±.002 .785 ±.007 .719 ±.005 .582 ±.043 .784 ±.007 .721 ±.002 .782 ±.003 2 .788 ±.006 .794 ±.006 .792 ±.007 .786 ±.007 .671 ±.004 .794 ±.006 .702 ±.004 .790 ±.005 .790 ±.006 .795 ±.005 .766 ±.040 .724 ±.022 .795 ±.008 .730 ±.000 .793 ±.003 3 .793 ±.005 .798 ±.006 .795 ±.009 .794 ±.006 .699 ±.022 .798 ±.006 .742 ±.046 .796 ±.003 .795 ±.006 .796 ±.005 .794 ±.015 .796 ±.008 .803 ±.009 .793 ±.006 .797 ±.009 4 .801 ±.005 .800 ±.009 .801 ±.007 .804 ±.007 .712 ±.015 .800 ±.009 .772 ±.038 .804 ±.007 .805 ±.006 .799 ±.005 .801 ±.008 .795 ±.009 .804 ±.008 .799 ±.003 .804 ±.009 5 .803 ±.005 .804 ±.004 .804 ±.008 .806 ±.005 .803 ±.004 .804 ±.004 .803 ±.011 .807 ±.006 .807 ±.007 .804 ±.005 .805 ±.009 .803 ±.007 .806 ±.006 .805 ±.007 .807 ±.006 porto seguro 1 .611 ±.001 .595 ±.025 .595 ±.015 .598 ±.004 .544 ±.004 .606 ±.014 .596 ±.028 .616 ±.003 .567 ±.021 .596 ±.009 .595 ±.002 .592 ±.009 .592 ±.009 .610 ±.001 .568 ±.010 2 .618 ±.001 .626 ±.005 .621 ±.005 .622 ±.001 .596 ±.004 .616 ±.012 .611 ±.013 .625 ±.001 .616 ±.003 .614 ±.006 .616 ±.004 .610 ±.007 .614 ±.007 .618 ±.000 .601 ±.006 3 .621 ±.001 .630 ±.004 .627 ±.002 .626 ±.001 .609 ±.003 .625 ±.005 .621 ±.009 .627 ±.000 .625 ±.002 .619 ±.003 .623 ±.002 .616 ±.002 .618 ±.005 .627 ±.001 .617 ±.007 4 .632 ±.002 .634 ±.002 .630 ±.002 .630 ±.002 .615 ±.005 .629 ±.006 .626 ±.008 .631 ±.003 .633 ±.002 .626 ±.003 .625 ±.002 .628 ±.001 .627 ±.004 .632 ±.002 .627 ±.003 5 .635 ±.001 .634 ±.003 .635 ±.002 .633 ±.001 .629 ±.003 .631 ±.005 .633 ±.001 .632 ±.001 .634 ±.002 .634 ±.002 .631 ±.004 .634 ±.001 .633 ±.002 .635 ±.002 .630 ±.004 japanese_credit screening 1 .838 ±.057 .742 ±.177 .788 ±.016 .807 ±.059 .687 ±.143 .827 ±.066 .676 ±.152 .783 ±.018 .781 ±.006 .804 ±.051 .746 ±.033 .572 ±.121 .674 ±.147 .786 ±.015 .676 ±.148 2 .813 ±.023 .787 ±.105 .818 ±.016 .775 ±.032 .729 ±.141 .842 ±.028 .737 ±.100 .768 ±.042 .765 ±.040 .823 ±.073 .798 ±.013 .684 ±.044 .714 ±.119 .794 ±.004 .730 ±.091 3 .815 ±.023 .826 ±.022 .825 ±.021 .789 ±.012 .727 ±.129 .822 ±.016 .733 ±.131 .784 ±.007 .789 ±.034 .835 ±.062 .793 ±.044 .725 ±.006 .719 ±.126 .807 ±.030 .795 ±.020 4 .817 ±.012 .836 ±.017 .838 ±.041 .797 ±.010 .773 ±.046 .836 ±.023 .781 ±.064 .809 ±.014 .806 ±.026 .836 ±.068 .818 ±.033 .811 ±.032 .774 ±.075 .808 ±.019 .784 ±.046 5 .823 ±.006 .828 ±.008 .834 ±.017 .811 ±.007 .824 ±.031 .823 ±.008 .794 ±.045 .820 ±.005 .814 ±.010 .845 ±.036 .828 ±.013 .825 ±.022 .796 ±.038 .813 ±.013 .792 ±.044 ionosphere 1 .967 ±.015 .944 ±.036 .964 ±.013 .965 ±.021 .935 ±.031 .938 ±.044 .967 ±.019 .968 ±.022 .972 ±.019 .967 ±.028 .965 ±.016 .967 ±.013 .975 ±.010 .972 ±.004 .963 ±.007 2 .975 ±.015 .973 ±.013 .983 ±.008 .981 ±.011 .963 ±.013 .972 ±.013 .974 ±.018 .980 ±.024 .985 ±.014 .981 ±.007 .978 ±.009 .974 ±.015 .976 ±.012 .981 ±.010 .975 ±.010 3 .980 ±.012 .980 ±.016 .982 ±.011 .981 ±.007 .974 ±.015 .980 ±.014 .979 ±.017 .983 ±.017 .984 ±.012 .984 ±.008 .982 ±.008 .983 ±.013 .981 ±.015 .983 ±.012 .979 ±.012 4 .983 ±.014 .983 ±.015 .983 ±.014 .983 ±.012 .981 ±.009 .985 ±.015 .982 ±.015 .984 ±.016 .984 ±.015 .982 ±.010 .980 ±.011 .980 ±.012 .981 ±.017 .983 ±.013 .980 ±.012 5 .982 ±.016 .985 ±.015 .983 ±.016 .983 ±.014 .983 ±.013 .985 ±.014 .983 ±.014 .985 ±.016 .984 ±.014 .982 ±.013 .983 ±.014 .985 ±.016 .982 ±.016 .982 ±.014 .982 ±.015 smoking lung_cancer 1 .671 ±.052 .682 ±.065 .696 ±.052 .686 ±.056 .673 ±.029 .663 ±.029 .663 ±.084 .680 ±.090 .631 ±.142 .700 ±.064 .716 ±.047 .593 ±.062 .579 ±.066 .738 ±.072 .645 ±.032 2 .789 ±.021 .752 ±.038 .716 ±.052 .729 ±.044 .719 ±.046 .760 ±.063 .686 ±.021 .704 ±.026 .735 ±.032 .707 ±.040 .729 ±.027 .676 ±.010 .701 ±.054 .730 ±.046 .721 ±.028 3 .756 ±.013 .799 ±.053 .707 ±.047 .737 ±.032 .733 ±.031 .774 ±.052 .737 ±.039 .709 ±.014 .750 ±.053 .729 ±.043 .744 ±.042 .726 ±.046 .720 ±.029 .743 ±.055 .735 ±.008 4 .765 ±.053 .778 ±.027 .726 ±.036 .733 ±.040 .726 ±.028 .800 ±.038 .744 ±.063 .718 ±.020 .709 ±.029 .743 ±.038 .757 ±.018 .717 ±.027 .738 ±.047 .738 ±.043 .722 ±.048 5 .731 ±.022 .763 ±.035 .720 ±.045 .711 ±.023 .694 ±.032 .758 ±.010 .714 ±.033 .693 ±.046 .678 ±.027 .743 ±.028 .719 ±.054 .692 ±.042 .725 ±.038 .728 ±.017 .722 ±.052 nci_pancreatic cancer 1 .625 ±.035 .696 ±.063 .639 ±.090 .618 ±.061 .548 ±.062 .558 ±.071 .610 ±.064 .514 ±.024 .577 ±.124 .548 ±.074 .624 ±.054 .477 ±.089 .455 ±.046 .604 ±.083 .521 ±.122 2 .627 ±.065 .758 ±.034 .670 ±.095 .592 ±.035 .540 ±.004 .674 ±.051 .578 ±.094 .613 ±.049 .558 ±.048 .757 ±.008 .681 ±.086 .581 ±.021 .558 ±.017 .683 ±.068 .615 ±.059 3 .663 ±.102 .753 ±.071 .671 ±.073 .607 ±.026 .552 ±.020 .719 ±.054 .631 ±.055 .584 ±.056 .618 ±.101 .703 ±.058 .714 ±.085 .660 ±.070 .648 ±.040 .724 ±.096 .632 ±.086 4 .642 ±.044 .727 ±.085 .559 ±.190 .621 ±.069 .541 ±.089 .722 ±.064 .658 ±.101 .595 ±.019 .589 ±.112 .727 ±.075 .673 ±.103 .602 ±.006 .611 ±.088 .735 ±.054 .620 ±.068 5 .669 ±.029 .725 ±.031 .601 ±.110 .610 ±.028 .595 ±.057 .664 ±.049 .620 ±.058 .580 ±.057 .573 ±.062 .714 ±.023 .645 ±.009 .621 ±.045 .665 ±.077 .638 ±.016 .651 ±.071 homesite_quote conversion 1 .902 ±.001 .816 ±.002 .874 ±.003 .706 ±.083 .501 ±.002 .816 ±.002 .717 ±.115 .880 ±.019 .891 ±.001 .901 ±.001 .839 ±.036 .599 ±.041 .593 ±.020 .902 ±.000 .840 ±.028 2 .933 ±.015 .927 ±.002 .885 ±.001 .865 ±.029 .607 ±.088 .918 ±.016 .849 ±.036 .920 ±.000 .909 ±.001 .908 ±.001 .920 ±.001 .724 ±.061 .725 ±.051 .912 ±.002 .827 ±.034 3 .957 ±.002 .951 ±.000 .910 ±.001 .915 ±.000 .645 ±.026 .950 ±.000 .880 ±.032 .933 ±.001 .928 ±.002 .920 ±.005 .939 ±.001 .771 ±.058 .778 ±.055 .956 ±.001 .898 ±.006 4 .962 ±.001 .962 ±.000 .955 ±.000 .932 ±.000 .697 ±.005 .959 ±.003 .918 ±.004 .935 ±.002 .941 ±.000 .940 ±.005 .946 ±.001 .867 ±.025 .853 ±.009 .960 ±.001 .908 ±.022 5 .962 ±.001 .964 ±.000 .959 ±.000 .939 ±.001 .751 ±.006 .963 ±.001 .934 ±.010 .939 ±.003 .963 ±.000 .963 ±.000 .957 ±.000 .945 ±.010 .921 ±.025 .960 ±.001 .937 ±.007 heart_disease va_long_beach 1 .585 ±.029 .628 ±.099 .665 ±.098 .641 ±.084 .677 ±.118 .634 ±.106 .630 ±.072 .655 ±.077 .633 ±.090 .622 ±.077 .641 ±.055 .576 ±.158 .605 ±.116 .570 ±.047 .639 ±.090 2 .615 ±.109 .636 ±.080 .676 ±.077 .685 ±.089 .652 ±.097 .636 ±.080 .646 ±.072 .648 ±.083 .665 ±.090 .622 ±.064 .635 ±.057 .708 ±.020 .690 ±.116 .631 ±.095 .650 ±.099 3 .659 ±.091 .652 ±.124 .666 ±.080 .662 ±.069 .690 ±.100 .653 ±.124 .665 ±.085 .695 ±.107 .688 ±.091 .640 ±.076 .642 ±.070 .677 ±.075 .698 ±.063 .674 ±.079 .667 ±.105 4 .697 ±.101 .647 ±.112 .692 ±.100 .699 ±.095 .699 ±.105 .650 ±.115 .694 ±.075 .689 ±.087 .684 ±.085 .654 ±.099 .681 ±.079 .676 ±.104 .714 ±.102 .716 ±.106 .696 ±.098 5 .707 ±.099 .657 ±.099 .692 ±.087 .703 ±.086 .706 ±.080 .654 ±.095 .716 ±.104 .700 ±.087 .701 ±.094 .696 ±.085 .704 ±.092 .695 ±.083 .717 ±.113 .709 ±.091 .715 ±.103 qsar_biodeg 1 .917 ±.018 .902 ±.006 .893 ±.021 .902 ±.006 .679 ±.059 .898 ±.016 .890 ±.037 .927 ±.008 .907 ±.025 .919 ±.008 .915 ±.003 .898 ±.014 .920 ±.018 .898 ±.019 .929 ±.020 2 .937 ±.012 .938 ±.006 .932 ±.012 .928 ±.007 .907 ±.009 .932 ±.003 .934 ±.009 .940 ±.009 .932 ±.009 .926 ±.009 .934 ±.006 .919 ±.015 .937 ±.008 .936 ±.007 .930 ±.018 3 .939 ±.009 .938 ±.008 .938 ±.010 .940 ±.008 .930 ±.009 .937 ±.010 .939 ±.013 .942 ±.011 .938 ±.010 .936 ±.011 .936 ±.007 .933 ±.016 .941 ±.007 .937 ±.008 .937 ±.014 4 .941 ±.009 .941 ±.009 .945 ±.010 .941 ±.012 .940 ±.010 .940 ±.009 .940 ±.013 .941 ±.011 .941 ±.010 .943 ±.010 .940 ±.007 .941 ±.014 .943 ±.006 .939 ±.009 .944 ±.012 5 .943 ±.010 .944 ±.009 .945 ±.010 .943 ±.011 .942 ±.012 .943 ±.009 .944 ±.010 .945 ±.011 .941 ±.011 .945 ±.012 .945 ±.011 .940 ±.009 .944 ±.009 .945 ±.011 .944 ±.010 naticusdroid_android permissions_dataset 1 .980 ±.001 .978 ±.005 .979 ±.001 .852 ±.051 .579 ±.002 .970 ±.011 .973 ±.006 .782 ±.093 .975 ±.005 .852 ±.051 .973 ±.002 .886 ±.068 .864 ±.089 .980 ±.001 .974 ±.002 2 .984 ±.001 .984 ±.002 .981 ±.001 .939 ±.002 .637 ±.014 .981 ±.004 .980 ±.003 .880 ±.048 .982 ±.001 .939 ±.002 .980 ±.001 .890 ±.008 .900 ±.010 .985 ±.001 .980 ±.005 3 .986 ±.001 .986 ±.002 .985 ±.002 .959 ±.009 .725 ±.008 .985 ±.002 .981 ±.004 .957 ±.006 .985 ±.002 .959 ±.009 .984 ±.001 .952 ±.007 .929 ±.052 .986 ±.001 .982 ±.004 4 .987 ±.001 .987 ±.002 .986 ±.002 .980 ±.004 .826 ±.003 .986 ±.001 .983 ±.003 .970 ±.001 .986 ±.002 .980 ±.004 .986 ±.001 .962 ±.027 .981 ±.008 .987 ±.001 .985 ±.002 5 .988 ±.001 .987 ±.001 .987 ±.002 .986 ±.001 .958 ±.006 .987 ±.002 .984 ±.002 .983 ±.003 .987 ±.001 .986 ±.001 .987 ±.001 .980 ±.006 .986 ±.003 .988 ±.001 .985 ±.004 76 (continued from previous page) DatasetBudget idx j CARTElasticNetF-testGainRatioLaplacianScoreLassoLOCO MarkovBlanket MImRMR(R)ReliefFRandomSBERFImportanceSFS framingham heart_study 1 .638 ±.062 .711 ±.025 .701 ±.020 .703 ±.021 .562 ±.016 .702 ±.025 .700 ±.022 .688 ±.029 .701 ±.021 .683 ±.025 .688 ±.020 .591 ±.018 .696 ±.017 .663 ±.038 .676 ±.014 2 .685 ±.025 .716 ±.023 .703 ±.018 .702 ±.020 .592 ±.015 .717 ±.024 .713 ±.030 .688 ±.020 .701 ±.022 .710 ±.012 .700 ±.031 .652 ±.018 .713 ±.024 .699 ±.019 .685 ±.012 3 .711 ±.024 .716 ±.024 .720 ±.020 .710 ±.025 .623 ±.028 .720 ±.026 .718 ±.025 .709 ±.025 .704 ±.018 .718 ±.022 .710 ±.025 .682 ±.022 .718 ±.023 .711 ±.024 .706 ±.017 4 .717 ±.020 .714 ±.022 .720 ±.020 .711 ±.024 .687 ±.049 .713 ±.027 .719 ±.025 .711 ±.025 .709 ±.020 .703 ±.030 .710 ±.023 .716 ±.028 .719 ±.022 .720 ±.025 .701 ±.016 5 .720 ±.023 .718 ±.021 .721 ±.022 .721 ±.022 .720 ±.022 .718 ±.023 .719 ±.020 .717 ±.023 .714 ±.024 .716 ±.024 .717 ±.018 .684 ±.046 .718 ±.020 .720 ±.022 .716 ±.026 77