TY  - GEN
N2  - 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.
DO  - 10.1007/978-3-031-12402-0
DO  - doi
AB  - 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.
T1  - Interpretability for Industry 4.0 :statistical and machine learning approaches /
AU  - Lepore, Antonio,
AU  - Palumbo, Biagio,
AU  - Poggi, Jean-Michel,
CN  - T59.6
ID  - 1450569
KW  - Industry 4.0.
KW  - Machine learning
KW  - Industry 4.0
SN  - 9783031124020
SN  - 3031124022
TI  - Interpretability for Industry 4.0 :statistical and machine learning approaches /
LK  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-12402-0
UR  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-12402-0
ER  -