Marginal effects for non-linear prediction functions

• Published in: Data mining and knowledge discovery Vol. 38; no. 5; pp. 2997 - 3042
• Main Authors: Scholbeck, Christian A., Casalicchio, Giuseppe, Molnar, Christoph, Bischl, Bernd, Heumann, Christian
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2024; Springer Nature B.V
• Subjects: Artificial Intelligence; Chemistry and Earth Sciences; Computer Science; Data Mining and Knowledge Discovery; Generalized linear models; Information Storage and Retrieval; Linear prediction; Machine learning; Nonlinearity; Physics; Regression models; Special Issue on Explainable and Interpretable Machine Learning and Data Mining; Statistical models; Statistics for Engineering; Subgroups; XAI; Interpretations; Model-agnostic; Interpretable machine learning; Conditional average marginal effects; IML; NLM; Non-linearity measure; Forward marginal effects; Explainable AI; Forward difference; cAME; FME

Beta coefficients for linear regression models represent the ideal form of an interpretable feature effect. However, for non-linear models such as generalized linear models, the estimated coefficients cannot be interpreted as a direct feature effect on the predicted outcome. Hence, marginal effects...