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. 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 will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

"Are you having trouble deciding whether or not to become a parent? Are you under pressure from family and friends? Unsettled by feelings of guilt or ambivalence? Unsure whether you will regret your decision in later years? The Baby Decision offers a clear path to finding the answers to all of these questions"--Back cover.