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Machine Learning Evaluation

Machine Learning Evaluation

Towards Reliable and Responsible AI

Zois Boukouvalas, Nathalie Japkowicz

Cambridge University Press
Publication date : 2024-11-21

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As machine learning applications gain widespread adoption and integration in a variety of applications, including safety and mission-critical systems, the need for robust evaluation methods grows more urgent. This book compiles scattered information on the topic from research papers and blogs to provide a centralized resource that is accessible to students, practitioners, and researchers across the sciences. The book examines meaningful metrics for diverse types of learning paradigms and applications, unbiased estimation methods, rigorous statistical analysis, fair training sets, and meaningful explainability, all of which are essential to building robust and reliable machine learning products. In addition to standard classification, the book discusses unsupervised learning, regression, image segmentation, and anomaly detection. The book also covers topics such as industry-strength evaluation, fairness, and responsible AI. Implementations using Python and scikit-learn are available on the book's website.

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About the book

Author

Zois Boukouvalas , Nathalie Japkowicz

Imprint

Cambridge University Press

Collection

n.c

Publication date

2024-11-21

Pages

n.c

Print ISBN

9781316518861

Language

English

Ebook informations

EAN PDF

9781009008549

Price

£63.29

Copy count 0

Print count 0

File size 9708 Ko

Informations accessibility

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About author(s)

Zois Boukouvalas

Nathalie Japkowicz

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