- 书名
Interpretable Machine Learning
- 作者Christoph Molnar
- 格式PDF
- ISBN书号9780244768522
- 出版年2019-3-24
- 出版社Lulu Press
- 页数318
- 定价USD 47.62
- 装帧Paperback
内容简介
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.
On a mission to make algorithms more interpretable by combining machine learning and statistics.
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