mlsanityData to 7 Oct 2026

Grouped Feature Importance and Combined Features Effect Plot

arXiv 2104.11688 ↗First posted 23 Apr 2021stat.ML cs.LGcited by 14

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Abstract (arXiv)

Interpretable machine learning has become a very active area of research due to the rising popularity of machine learning algorithms and their inherently challenging interpretability. Most work in this area has been focused on the interpretation of single features in a model. However, for researchers and practitioners, it is often equally important to quantify the importance or visualize the effect of feature groups. To address this research gap, we provide a comprehensive overview of how existing model-agnostic techniques can be defined for feature groups to assess the grouped feature importance, focusing on permutation-based, refitting, and Shapley-based methods. We also introduce an importance-based sequential procedure that identifies a stable and well-performing combination of features in the grouped feature space. Furthermore, we introduce the combined features effect plot, which is a technique to visualize the effect of a group of features based on a sparse, interpretable linear combination of features. We used simulation studies and a real data example from computational psychology to analyze, compare, and discuss these methods.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 17 of its 108 references that are in the map. First posted on arXiv 23 Apr 2021.

Builds on

17 of 108 references.

References · 17

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2016The Mythos of Model Interpretabilitycited by 716
  3. 2016Distribution-Free Predictive Inference For Regressioncited by 657
  4. 2018All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneouslycited by 364
  5. 2018Consistent Individualized Feature Attribution for Tree Ensemblescited by 279
  6. 2016Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Modelscited by 212
  7. 2020General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Modelscited by 74
  8. 2020Efficient nonparametric statistical inference on population feature importance using Shapley valuescited by 61
  9. 2018Visualizing the Feature Importance for Black Box Modelscited by 55
  10. 2016Kernel-based Tests for Joint Independencecited by 40
  11. 2020Model-agnostic Feature Importance and Effects with Dependent Features -- A Conditional Subgroup Approachcited by 37
  12. 2019Testing Conditional Independence in Supervised Learning Algorithmscited by 27
5 more
  1. 2014Grouped variable importance with random forests and application to multiple functional data analysiscited by 16
  2. 2019Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretationscited by 9
  3. 2020Human-interpretable model explainability on high-dimensional datacited by 8
  4. 2021The Shapley Value of coalition of variables provides better explanationscited by 3
  5. 2018Structured nonlinear variable selectioncited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 5; 2024: 2; 2025: 3; 2026: 2.

14 papers.

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  1. 2022Comparing Explanation Methods for Traditional Machine Learning Models Part 1: An Overview of Current Methods and Quantifying Their Disagreementcited by 14
  2. 2024A Guide to Feature Importance Methods for Scientific Inferencecited by 11
  3. 2024Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theorycited by 10
  4. 2022Conditional Feature Importance for Mixed Datacited by 7
  5. 2023Interpretable machine learning for time-to-event prediction in medicine and healthcarecited by 7
  6. 2025shapr: Explaining Machine Learning Models with Conditional Shapley Values in R and Pythoncited by 5
  7. 2023Variable Importance in High-Dimensional Settings Requires Groupingcited by 4
  8. 2023Explaining and visualizing black-box models through counterfactual pathscited by 2
  9. 2026GRANITE: A Generalized Regional Framework for Identifying Agreement in Feature-Based Explanationscited by 1
  10. 2026xplainfi: Feature Importance and Statistical Inference for Machine Learning in Rcited by 1
  11. 2023Explainable Modeling for Wind Power Forecasting: A Glass-Box Approach with High Accuracycited by 0
  12. 2023Improving the Accuracy and Interpretability of Neural Networks for Wind Power Forecastingcited by 0
2 more
  1. 2025The Most Important Features in Generalized Additive Models Might Be Groups of Featurescited by 0
  2. 2025Beyond Additivity: Sparse Isotonic Shapley Regression toward Nonlinear Explainabilitycited by 0