Interpretable Machine Learning - A Guide for Making Black Box Models Interpretable - Grand Format

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 leam about simple, interpretable models such as decision trees, decision rules and linear regression.
Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood ? What are their strengths and weaknesses ? How can their outputs be interpreted ? This book enables you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
The book focuses on interpretation methods for machine learning models trained on tabular data. Reading the book is recommended for machine learning practitioners, data scientists, statisticians, and anyone else interested in making machine learning models interpretable.