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Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches / edited by Antonio Lepore, Biagio Palumbo, Jean-Michel Poggi.
other author:	Lepore, Antonio.
Description:	VII, 123 p. 45 illus., 32 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Statistics . -
Online resource:	https://doi.org/10.1007/978-3-031-12402-0
ISBN:	9783031124020

Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches
Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches [electronic resource] /edited by Antonio Lepore, Biagio Palumbo, Jean-Michel Poggi. - 1st ed. 2022. - VII, 123 p. 45 illus., 32 illus. in color.online resource.

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.

ISBN: 9783031124020

Standard No.: 10.1007/978-3-031-12402-0doiSubjects--Topical Terms:

1253516
Statistics .

LC Class. No.: QA276-280

Dewey Class. No.: 519.5

Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches
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