Interpretable Machine Learning: A Guide For Making Black Box Models Explainable  (Paperback, Christoph Molnar)

Machine learning has great potential for improving products, processes and research. But computers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. The focus of the book is on model-agnostic methods for interpreting black box models such as feature importance and accumulated local ects, and explaining individual predictions with Shapley values and LIME. In addition, the book presents methods specific to deep neural networks.