Interpretable Machine Learning by Christoph Molnar .pdf
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Interpretable Machine Learning
Interpretable Machine Learning
A Guide for Making Black Box Models Explainable.
Christoph Molnar
2021-03-12
Summary
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. 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.
The book focuses on machine learning models for tabular data (also called relational or structured data) and less on
computer vision and natural language processing tasks. Reading the book is recommended for machine learning
practitioners, data scientists, statisticians, and anyone else interested in making machine learning models interpretable.
You can buy the PDF and e-book version (epub, mobi) on leanpub.com.
You can buy the print version on lulu.com.
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田祥2022-07-25这个版本还不错，能够满足我的需要 #内容详尽
