4 citations · 6 across the 9 of their papers we have counts for
4 papers · 1 filter
SAILS: Surrogate-based Analysis of Interactions via Local Effect Smooths
Timo Heiß, 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…
Analyzing Error Sources in Global Feature Effect Estimation
Timo Heiß, Coco Bögel, Bernd Bischl +1
Global feature effects such as partial dependence (PD) and accumulated local effects (ALE) plots are widely used to interpret black-box models. However, they are only estimates of…
A Guide to Feature Importance Methods for Scientific Inference
Fiona Katharina Ewald, Ludwig Bothmann, Marvin N. Wright +3
While machine learning (ML) models are increasingly used due to their high predictive power, their use in understanding the data-generating process (DGP) is limited. Understanding…
Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation
Julia Herbinger, Susanne Dandl, Fiona K. Ewald +2
Surrogate models play a crucial role in retrospectively interpreting complex and powerful black box machine learning models via model distillation. This paper focuses on using mode…