6 papers
SAILS: Surrogate-based Analysis of Interactions via Local Effect Smooths
Timo HeiÃ, Julia Herbinger, Bernd Bischl +1
Feature interactions drive much of the predictive power of machine learning models, yet existing explanation methods only detect and quantify interactions without revealing their f…
Functional Decomposition and Shapley Interactions for Interpreting Survival Models
Sophie Hanna Langbein, Hubert Baniecki, Fabian Fumagalli +3
Hazard and survival functions are natural, interpretable targets in time-to-event prediction, but their inherent non-additivity fundamentally limits standard additive explanation m…
GRANITE: A Generalized Regional Framework for Identifying Agreement in Feature-Based Explanations
Julia Herbinger, Gabriel Laberge, Maximilian Muschalik +3
Feature-based explanation methods aim to quantify how features influence the model's behavior, either locally or globally, but different methods often disagree, producing conflicti…
Effector: A Python package for regional explanations
Vasilis Gkolemis, Christos Diou, Dimitris Kyriakopoulos +10
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 Partia…
Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory
Fabian Fumagalli, Maximilian Muschalik, Eyke Hüllermeier +2
Feature-based explanations, using perturbations or gradients, are a prevalent tool to understand decisions of black box machine learning models. Yet, differences between these meth…
Decomposing Global Feature Effects Based on Feature Interactions
Julia Herbinger, Marvin N. Wright, Thomas Nagler +2
Global feature effect methods, such as partial dependence plots, provide an intelligible visualization of the expected marginal feature effect. However, such global feature effect…