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japkowicz n machine learning evaluation towards reliable responsible ai 2025

Type:: E-books
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Size:: 18.0 MB
Uploaded On:: Nov. 13, 2024, 7:59 a.m.
Added By:: andryold1
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Info Hash:: 4F7B0E987F4787AE7DBD78BB641805E8B2CF4C00

Textbook in PDF format 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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Japkowicz N. Machine Learning Evaluation. Towards Reliable...Responsible AI 2025.pdf 18.0 MB

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japkowicz n machine learning evaluation towards reliable responsible ai 2025