no code implementations • 20 Dec 2023 • Christian A. Scholbeck, Julia Moosbauer, Giuseppe Casalicchio, Hoshin Gupta, Bernd Bischl, Christian Heumann
We argue that interpretations of machine learning (ML) models or the model-building process can bee seen as a form of sensitivity analysis (SA), a general methodology used to explain complex systems in many fields such as environmental modeling, engineering, or economics.
no code implementations • 3 Oct 2023 • Holger Löwe, Christian A. Scholbeck, Christian Heumann, Bernd Bischl, Giuseppe Casalicchio
Forward marginal effects (FMEs) have recently been introduced as a versatile and effective model-agnostic interpretation method.
no code implementations • 21 Sep 2022 • Christian A. Scholbeck, Henri Funk, Giuseppe Casalicchio
The partial dependence for clustering evaluates average changes in cluster assignments for the entire feature space.
no code implementations • 21 Jan 2022 • Christian A. Scholbeck, Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl, Christian Heumann
Hence, marginal effects are typically used as approximations for feature effects, either in the shape of derivatives of the prediction function or forward differences in prediction due to a change in a feature value.
1 code implementation • 8 Jul 2020 • Christoph Molnar, Gunnar König, Julia Herbinger, Timo Freiesleben, Susanne Dandl, Christian A. Scholbeck, Giuseppe Casalicchio, Moritz Grosse-Wentrup, Bernd Bischl
An increasing number of model-agnostic interpretation techniques for machine learning (ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) and Shapley values provide insightful model interpretations, but can lead to wrong conclusions if applied incorrectly.
2 code implementations • 8 Apr 2019 • Christian A. Scholbeck, Christoph Molnar, Christian Heumann, Bernd Bischl, Giuseppe Casalicchio
Model-agnostic interpretation techniques allow us to explain the behavior of any predictive model.