Interpretable Machine Learning

A Guide for Making Black Box Models Explainable



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Interpretable Machine Learning
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AuthorChristoph Molnar
PublisherLeanpub
Published2020
Pages312
LanguageEnglish
FormatPaper book / ebook (PDF)
ISBN-100244768528
ISBN-139780244768522
EBook Hardcover Paperback

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.





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