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:	411
Created:	2022-02-03
License:	CC BY-NC-SA
Author(s):	Christoph Molnar

Warning: Trying to access array offset on false in /home/tutovnfz/public_html/amp/article-amp.php on line 263

Download file

Others related eBooks about Interpretable Machine Learning

An Introduction to Machine Learning

Download free course An Introduction to Machine Learning, pdf file on 348 pages by by Miroslav Kubat.

A Brief Introduction to Machine Learning for Engineers

This book aims at providing an introduction to key concepts, algorithms, and theoretical frameworks in machine learning, including supervised and unsupervised learning, statistical learning theory, probabilistic graphical models and approximate inference. The intended readership consists of electric

Interpretable Machine Learning: A Guide for Making Black Box Models Explainable

This book explains to you how to make (supervised) machine learning models interpretable.

Machine Learning for Cyber Physical Systems

This Open Access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, October 23-24, 2018.

Interpretable Machine Learning

This book is about making machine learning models and their decisions interpretable. After..., download free Machine Learning tutorial in PDF (312 pages) created by Christoph Molnar .