Evaluating Learning Algorithms: A Classification Perspective

The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA facilitating better practical insight as well as implementation. Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for conducting performance evaluations of algorithms in practical settings.

Author: Nathalie Japkowicz, Mohak Shah
Publisher: Cambridge University Press
Published: 01/17/2011
Pages: 424
Binding Type: Hardcover
Weight: 1.49lbs
Size: 9.30h x 6.40w x 1.10d
ISBN: 9780521196000

About the Author
Japkowicz, Nathalie: - Nathalie Japkowicz is Professor of Computer Science at American University. She is a former assistant professor at Dalhousie University and lecturer at Ohio State University. Japkowicz co-organized numerous workshops on classifier evaluation and the class imbalance problem at AAAI and ICML. She has published many articles in peer-reviewed journals and conference proceedings.Shah, Mohak: - Mohak Shah is an AI and technology executive with extensive experience in bringing data and AI products to market. He has held several senior leadership roles in large enterprises and startups driving both large-scale AI transformation initiatives and zero-to-one product journeys. He is the founder and Managing Director of Praescivi Advisors, a strategic AI advisory practice. As a research scientist, Mohak has published extensively in theoretical and applied machine learning areas.

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