Effector: A Python Package for Regional Explanations

MCML Authors

Julia Herbinger

Dr.

* Former Member

→ Group Bernd Bischl
 Statistical Learning and Data Science

Maximilian Muschalik

→ Group Eyke Hüllermeier
 Artificial Intelligence and Machine Learning

Bernd Bischl

Prof. Dr.

Director

Statistical Learning and Data Science

Giuseppe Casalicchio

Dr.

→ Group Bernd Bischl
 Statistical Learning and Data Science

Abstract

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.

misc GDK+25

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Preprint

Nov. 2025

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Authors

V. Gkolemis • C. Diou • D. Kyriakopoulos • K. Tsopelas • J. Herbinger • H. Baniecki • D. Rontogiannis • L. Kavouras • M. Muschalik • T. Dalamagas • E. Ntoutsi • B. Bischl • G. Casalicchio

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Links

arXiv GitHub

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Research Areas

 A1 | Statistical Foundations & Explainability

 A3 | Computational Models