Interpretable Machine Learning

Interpretable Machine Learning Molnar,Christoph Additional Author mixed material bibliography Germany westendstrafie Munchen, Germany 2022 en English x, 318 pages ; 24 cm 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 Molnar,Christoph 1. Computer Science 2. Software Engineering 3. Dat Data Science Information Technology 006.3 979811463330 NUML LIBRARY RAWALPINDI (National University of Modern Languages) NUML library is the state of art which equipped “RESEARCH FACILITATION CENTRE” with latest computers for readers to access the digital library of more than 23000 research journals and 130000 online books and E-Library of NUML-Rawalpindi. 006.3 MOL 14235 14235.jpg.jpg 21489 2026-02-02 16:13:07 2026-02-03 16:06:03 machine generated