Book

Interpretable Machine Learning

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Summary

This book covers a range of interpretability methods, from inherently interpretable models to methods that can make any model interpretable, such as SHAP, LIME and permutation feature importance. It also includes interpretation methods specific to deep neural networks, and discusses why interpretability is important in machine learning. 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?

"What I love about this book is that it starts with the big picture instead of diving immediately into the nitty gritty of the methods (although all of that is there, too)."
? Andrea Farnham, Researcher at Swiss Tropical and Public Health Institute

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Availability

Detail Information

Series Title

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Call Number

006.3 MOL

Publisher

Germany : westendstrafie Munchen, Germany., 2022

Collation

x, 318 pages ; 24 cm

Language

English

ISBN/ISSN

979811-46-3330

Classification

006.3

Content Type

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Media Type

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Carrier Type

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Edition

3rd Edition

Subject(s)

1. Computer Science 2. Software Engineering 3. Dat
Data Science Information Technology

Specific Detail Info

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Statement of Responsibility

Molnar,Christoph

Other version/related

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