5 citations · 13 across the 5 of their papers we have counts for
7 papers
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…
REPID: Regional Effect Plots with implicit Interaction Detection
Julia Herbinger, Bernd Bischl, Giuseppe Casalicchio
Machine learning models can automatically learn complex relationships, such as non-linear and interaction effects. Interpretable machine learning methods such as partial dependence…
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,…
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…
Component-Wise Boosting of Targets for Multi-Output Prediction
Quay Au, Daniel Schalk, Giuseppe Casalicchio +3
Multi-output prediction deals with the prediction of several targets of possibly diverse types. One way to address this problem is the so called problem transformation method. This…
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…