Machine Learning Evaluation : Towards Reliable and Responsible AI

Machine Learning Evaluation : Towards Reliable and Responsible AI

(Autor) Nathalie Japkowicz
Formato: Hardcover
59,99 Precio: £54,99 (8% off)
In Stock

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.

Information
Editorial:
Cambridge University Press
Formato:
Hardcover
Número de páginas:
None
ISBN:
9781316518861
Año de publicación:
2024
Fecha publicación:
21 de Noviembre de 2024

Nathalie Japkowicz

Nathalie Japkowicz is a renowned Canadian author known for her groundbreaking work in the field of artificial intelligence and machine learning. Her most famous book, "Big Data and Machine Learning for Predictive Analytics," explores the intersection of data science and literature. Japkowicz's writing style is characterized by its clarity and depth, making complex concepts accessible to a wide audience. Her key contributions to literature lie in her ability to bridge the gap between technology and storytelling, inspiring readers to think critically about the impact of data on society.

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