38 citations · 40 across the 3 of their papers we have counts for
4 papers
CASHomon Sets: Efficient Rashomon Sets Across Multiple Model Classes and their Hyperparameters
Fiona Katharina Ewald, Martin Binder, Matthias Feurer +2
Rashomon sets are model sets within one model class that perform nearly as well as a reference model from the same model class. They reveal the existence of alternative well-perfor…
xplainfi: Feature Importance and Statistical Inference for Machine Learning in R
Lukas Burk, Fiona Katharina Ewald, Giuseppe Casalicchio +2
We introduce xplainfi, an R package built on top of the mlr3 ecosystem for global, loss-based feature importance methods for machine learning models. Various feature importance met…
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…