Interpretable Machine Learning

Download free course Interpretable Machine Learning, pdf file on 312 pages by Christoph Molnar.

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.

Table of contents

Introduction
Interpretability
Datasets
Interpretable Models
Model-Agnostic Methods
Example-Based Explanations
A Look into the Crystal Ball

Pages :	312
Size :	8.7 MB
Downloads:	396
Created:	2022-02-03
License:	CC BY-NC-SA
Author(s):	Christoph Molnar

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