Limitations of Interpretable Machine Learning Methods

This book explains limitations of current methods in interpretable machine learning. The methods include partial dependence plots (PDP), Accumulated Local Effects (ALE), permutation feature importance, leave-one-covariate out (LOCO) and local interpretable model-agnostic explanations (LIME). All of those methods can be used to explain the behavior and predictions of trained machine learning models. This book is the outcome of the seminar "Limitations of Interpretable Machine Learning" which took place in summer 2019 at the Department of Statistics, LMU Munich.