Decision List

Decision List
Author: United States Board on Geographical Names
Publisher:
Total Pages: 534
Release: 1945
Genre: Geography
ISBN:

Decision List

Decision List
Author: United States Board on Geographic Names
Publisher:
Total Pages: 700
Release: 1939
Genre: Geography
ISBN:

Interpretable Machine Learning

Interpretable Machine Learning
Author: Christoph Molnar
Publisher: Lulu.com
Total Pages: 320
Release: 2020
Genre: Computers
ISBN: 0244768528

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.

Computational Learning Theory

Computational Learning Theory
Author: Shai Ben-David
Publisher: Springer Science & Business Media
Total Pages: 350
Release: 1997-03-03
Genre: Computers
ISBN: 9783540626855

Content Description #Includes bibliographical references and index.

Computational Learning Theory

Computational Learning Theory
Author: Paul Vitanyi
Publisher: Springer Science & Business Media
Total Pages: 442
Release: 1995-02-23
Genre: Computers
ISBN: 9783540591191

This volume presents the proceedings of the Second European Conference on Computational Learning Theory (EuroCOLT '95), held in Barcelona, Spain in March 1995. The book contains full versions of the 28 papers accepted for presentation at the conference as well as three invited papers. All relevant topics in fundamental studies of computational aspects of artificial and natural learning systems and machine learning are covered; in particular artificial and biological neural networks, genetic and evolutionary algorithms, robotics, pattern recognition, inductive logic programming, decision theory, Bayesian/MDL estimation, statistical physics, and cryptography are addressed.