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Relative Feature Importance
Gunnar König, Christoph Molnar, Bernd Bischl +1
Interpretable Machine Learning (IML) methods are used to gain insight into the relevance of a feature of interest for the performance of a model. Commonly used IML methods differ i…
Multi-Objective Counterfactual Explanations
Susanne Dandl, Christoph Molnar, Martin Binder +1
Counterfactual explanations are one of the most popular methods to make predictions of black box machine learning models interpretable by providing explanations in the form of `wha…
Visualizing the Feature Importance for Black Box Models
Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl
In recent years, a large amount of model-agnostic methods to improve the transparency, trustability and interpretability of machine learning models have been developed. We introduc…