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＜電子ブック＞
Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches / edited by Antonio Lepore, Biagio Palumbo, Jean-Michel Poggi

版	1st ed. 2022.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2022
本文言語	英語
大きさ	VII, 123 p. 45 illus., 32 illus. in color : online resource
著者標目	Lepore, Antonio editor Palumbo, Biagio editor Poggi, Jean-Michel editor SpringerLink (Online service)
件　名	LCSH:Statistics  FREE:Statistical Theory and Methods FREE:Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences FREE:Statistics in Business, Management, Economics, Finance, Insurance
一般注記	This volume provides readers with a compact, stimulating and multifaceted introduction to interpretability, a key issue for developing insightful statistical and machine learning approaches as well as for communicating modelling results in business and industry. Different views in the context of Industry 4.0 are offered in connection with the concepts of explainability of machine learning tools, generalizability of model outputs and sensitivity analysis. Moreover, the book explores the integration of Artificial Intelligence and robust analysis of variance for big data mining and monitoring in Additive Manufacturing, and sheds new light on interpretability via random forests and flexible generalized additive models together with related software resources and real-world examples HTTP:URL=https://doi.org/10.1007/978-3-031-12402-0

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