mlsanityData to 7 Oct 2026

Effector: A Python package for regional explanations

arXiv 2404.02629 ↗First posted 3 Apr 2024cs.LGcited by 1

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Abstract (arXiv)

Effector is a Python package for interpreting machine learning (ML) models that are trained on tabular data through global and regional feature effects. Global effects, like Partial Dependence Plot (PDP) and Accumulated Local Effects (ALE), are widely used for explaining tabular ML models due to their simplicity -- each feature's average influence on the prediction is summarized by a single 1D plot. However, when features are interacting, global effects can be misleading. Regional effects address this by partitioning the input space into disjoint subregions with minimal interactions within each and computing a separate regional effect per subspace. Regional effects are then visualized by a set of 1D plots per feature. Effector provides efficient implementations of state-of-the-art global and regional feature effects methods under a unified API. The package integrates seamlessly with major ML libraries like scikit-learn and PyTorch. It is designed to be modular and extensible, and comes with comprehensive documentation and tutorials. Effector is an open-source project publicly available on Github at https://github.com/givasile/effector.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 18 of its 26 references that are in the map. First posted on arXiv 3 Apr 2024.

Builds on

18 of 26 references.

References · 18

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  12. 2023Decomposing Global Feature Effects Based on Feature Interactionscited by 17
6 more
  1. 2019VINE: Visualizing Statistical Interactions in Black Box Modelscited by 13
  2. 2022DALE: Differential Accumulated Local Effects for efficient and accurate global explanationscited by 11
  3. 2020Surrogate Locally-Interpretable Models with Supervised Machine Learning Algorithmscited by 10
  4. 2023RHALE: Robust and Heterogeneity-aware Accumulated Local Effectscited by 8
  5. 2022Marginal Effects for Non-Linear Prediction Functionscited by 5
  6. 2023Regionally Additive Models: Explainable-by-design models minimizing feature interactionscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026We Need Explanation Cards to Connect Explanation Algorithms to the Real Worldcited by 0