Download the fantastic book titled Interpretable Machine Learning written by Christoph Molnar, available in its entirety in both PDF and EPUB formats for online reading. This page includes a concise summary, a preview of the book cover, and detailed information about "Interpretable Machine Learning", which was released on 27 May 2024. We suggest perusing the summary before initiating your download. This book is a top selection for enthusiasts of the Artificial intelligence genre.
Summary of Interpretable Machine Learning by Christoph Molnar PDF
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.
Detail About Interpretable Machine Learning PDF
- Author : Christoph Molnar
- Publisher : Lulu.com
- Genre : Artificial intelligence
- Total Pages : 320 pages
- ISBN : 0244768528
- Release Date : 27 May 2024
- PDF File Size : 18,7 Mb
- Language : English
- Rating : 4.5/5 from 2 reviews
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