112 citations · 164 across the 6 of their papers we have counts for
6 papers
Validity Threats for Foundation Model Research
Gunnar König, Martin Pawelczyk, Ulrike von Luxburg +1
Controlled experiments are the backbone of machine learning research, but at the scale of modern foundation models, they have become prohibitively expensive. Instead, the community…
CountARFactuals -- Generating plausible model-agnostic counterfactual explanations with adversarial random forests
Susanne Dandl, Kristin Blesch, Timo Freiesleben +4
Counterfactual explanations elucidate algorithmic decisions by pointing to scenarios that would have led to an alternative, desired outcome. Giving insight into the model's behavio…
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…
Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT)
Gunnar König, Timo Freiesleben, Bernd Bischl +2
Global model-agnostic feature importance measures either quantify whether features are directly used for a model's predictions (direct importance) or whether they contain predictio…
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models
Christoph Molnar, Gunnar König, Julia Herbinger +6
An increasing number of model-agnostic interpretation techniques for machine learning (ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) and S…
Model-agnostic Feature Importance and Effects with Dependent Features -- A Conditional Subgroup Approach
Christoph Molnar, Gunnar König, Bernd Bischl +1
The interpretation of feature importance in machine learning models is challenging when features are dependent. Permutation feature importance (PFI) ignores such dependencies, whic…