35 citations · 52 across the 5 of their papers we have counts for
5 papers
fmeffects: An R Package for Forward Marginal Effects
Holger Löwe, Christian A. Scholbeck, Christian Heumann +2
Forward marginal effects have recently been introduced as a versatile and effective model-agnostic interpretation method particularly suited for non-linear and non-parametric predi…
Algorithm-Agnostic Interpretations for Clustering
Christian A. Scholbeck, Henri Funk, Giuseppe Casalicchio
A clustering outcome for high-dimensional data is typically interpreted via post-processing, involving dimension reduction and subsequent visualization. This destroys the meaning o…
Marginal Effects for Non-Linear Prediction Functions
Christian A. Scholbeck, Giuseppe Casalicchio, Christoph Molnar +2
Beta coefficients for linear regression models represent the ideal form of an interpretable feature effect. However, for non-linear models and especially generalized linear models,…
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…
Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations
Christian A. Scholbeck, Christoph Molnar, Christian Heumann +2
Model-agnostic interpretation techniques allow us to explain the behavior of any predictive model. Due to different notations and terminology, it is difficult to see how they are r…