Interpretable Machine Learning: A Guide For Making Black Box Models Explainable
February 28, 2022
by Christoph Molnar(Author)
ASIN: B09TMWHVB4
Publisher finelybook 出版社: Independently published (February 28, 2022)
Language 语言: English
Print Length 页数: 328 pages
ISBN-13: 9798411463330
Book Description
By finelybook
If you’re looking for a book that will help you make machine learning models explainable, look no further than Interpretable Machine Learning.
This book provides a clear and concise explanation of the methods and mathematics behind the most important approaches to making machine learning models intepretable.
You’ll learn about:
Inherently interpretable models
Methods that can make any machine model interpretable, such as SHAP, LIME and permutation feature importance.
Interpretation methods specific to deep neural networks
Why interpretability is important and what’s behind this concept
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. Reading the book is recommended for machine learning practitioners, data scientists, statisticians, and anyone interested in making machine learning models interpretable.
About the author:
Christoph Molnar is an expert in machine learning and statistics. He did his Ph.D. in interpretable machine learning.
