Synopsis
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.
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About the Authors
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.
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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Evaluating Learning Algorithms: A Classification Perspective
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Hardcover. Condition: new. Hardcover. 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. This book gives a solid basis for conducting performance evaluations of learning algorithms in practical settings with an emphasis on classification algorithms. The authors describe several techniques designed to deal with performance measures and methods, error estimation or re-sampling techniques, statistical significance testing, data set selection and evaluation benchmark design. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9780521196000
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Hardcover. Condition: new. Hardcover. 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. This book gives a solid basis for conducting performance evaluations of learning algorithms in practical settings with an emphasis on classification algorithms. The authors describe several techniques designed to deal with performance measures and methods, error estimation or re-sampling techniques, statistical significance testing, data set selection and evaluation benchmark design. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9780521196000
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