Interpretable Machine Learning
Interpretable Machine Learning

Author: Christoph Molnar

Publisher: Lulu.com

Published: 2020

Total Pages: 320

ISBN-13: 0244768528

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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

Published: 1997-03-03

Total Pages: 350

ISBN-13: 9783540626855

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Content Description #Includes bibliographical references and index.

Data Mining and Knowledge Discovery Handbook
Data Mining and Knowledge Discovery Handbook

Author: Oded Z. Maimon

Publisher: Springer Science & Business Media

Published: 2005

Total Pages: 1436

ISBN-13: 9780387244358

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Organizes major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD). This book provides algorithmic descriptions of classic methods, and also suitable for professionals in fields such as computing applications, information systems management, and more.

Computational Learning Theory
Computational Learning Theory

Author: Paul Vitanyi

Publisher: Springer Science & Business Media

Published: 1995-02-23

Total Pages: 442

ISBN-13: 9783540591191

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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.