Interpretability for Industry 4.0: Statistical and Machine Learning Approaches

Edited by Antonio Lepore (), Biagio Palumbo () and Jean-Michel Poggi ()

in Springer Books from Springer

Date: 2022
ISBN: 978-3-031-12402-0
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Chapters in this book:

Ch Chapter 1 Different Views of Interpretability

Bertrand Iooss, Ron Kenett and Piercesare Secchi

Ch Chapter 2 Model Interpretability, Explainability and Trust for Manufacturing 4.0

Bianca Maria Colosimo and Fabio Centofanti

Ch Chapter 3 Interpretability via Random Forests

Clément Bénard, Sébastien Da Veiga and Erwan Scornet

Ch Chapter 4 Interpretability in Generalized Additive Models

S. N. Wood, Y. Goude and M. Fasiolo

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprbok:978-3-031-12402-0

Ordering information: This item can be ordered from
http://www.springer.com/9783031124020

DOI: 10.1007/978-3-031-12402-0

Access Statistics for this book

More books in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().