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Biblioteca

MARC Lista (tabellare)
Interpretability for Industry 4.0 : statistical and machine learning approaches / / Antonio Lepore, Biagio Palumbo, Jean-Michel Poggi, editors
Interpretability for Industry 4.0 : statistical and machine learning approaches / / Antonio Lepore, Biagio Palumbo, Jean-Michel Poggi, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (130 pages) : illustrations
Disciplina 658.4038028563
Soggetto topico Industry 4.0
Machine learning - Industrial applications
Industry 4.0 - Statistical methods
Aprenentatge automàtic
Aplicacions industrials
Soggetto genere / forma Llibres electrònics
ISBN 3-031-12402-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- 1 Different Views of Interpretability -- 1.1 Introduction -- 1.2 Interpretability: In Praise of Transparent Models -- 1.2.1 What Happened? -- 1.2.2 What Will Happen? -- 1.2.3 What Shall be Done to Make It Happen? -- 1.2.4 Patterns and Models -- 1.3 Generalizability and Interpretability with Industry 4.0 Implications -- 1.3.1 Introduction to Interpretable AI -- 1.3.2 A Wide Angle Perspective of Generalizability -- 1.3.3 Statistical Generalizability -- 1.4 Connections Between Interpretability in Machine Learning and Sensitivity Analysis of Model Outputs -- 1.4.1 Machine Learning and Uncertainty Quantification -- 1.4.2 Basics on Sensitivity Analysis and Its Main Settings -- 1.4.3 A Brief Taxonomy of Interpretability in Machine Learning -- 1.4.4 A Review of Sensitivity Analysis Powered Interpretability Methods -- References -- 2 Model Interpretability, Explainability and Trust for Manufacturing 4.0 -- 2.1 Manufacturing 4.0: Driving Trends for Data Mining -- 2.1.1 Process Monitoring in Manufacturing 4.0 -- 2.1.2 Design of Experiments in Manufacturing 4.0 -- 2.1.3 Increasing Trust in AI Models for Manufacturing 4.0: Interpretability, Explainability and Robustness -- 2.2 Additive Manufacturing as a Paradigmatic Example of Manufacturing 4.0 -- 2.3 Increase Trust in Additive Manufacturing: Robust Functional Analysis of Variance in Video-Image Analysis -- 2.3.1 The RoFANOVA Approach -- 2.3.2 An Additive Manufacturing Application -- References -- 3 Interpretability via Random Forests -- 3.1 Introduction -- 3.2 Interpretable Rule-Based Models -- 3.2.1 Literature Review -- 3.2.1.1 Definitions and Origins of Rule Models -- 3.2.1.2 Decision Trees -- 3.2.1.3 Tree-Based Rule Learning -- 3.2.1.4 Modern Rule Learning -- 3.2.2 SIRUS: Stable and Interpretable RUle Set -- 3.2.2.1 SIRUS Algorithm -- 3.2.2.2 Theoretical Analysis.
3.2.2.3 Experiments -- 3.2.3 Discussion -- 3.3 Post-Processing of Black-Box Algorithms via Variable Importance -- 3.3.1 Literature Review -- 3.3.1.1 Model-Specific Variable Importance -- 3.3.1.2 Global Sensitivity Analysis -- 3.3.1.3 Local Interpretability -- 3.3.2 Sobol-MDA -- 3.3.2.1 Sobol-MDA Algorithm -- 3.3.2.2 Sobol-MDA Properties -- 3.3.2.3 Experiments -- 3.3.3 SHAFF: SHApley eFfects Estimates via Random Forests -- 3.3.3.1 SHAFF Algorithm -- 3.3.3.2 SHAFF Consistency -- 3.3.3.3 Experiments -- 3.3.4 Discussion -- References -- 4 Interpretability in Generalized Additive Models -- 4.1 GAMs: A Basic Framework for Flexible Interpretable Regression -- 4.1.1 Flexibility Can Be Important -- 4.1.2 Making the Model Computable -- 4.1.3 Estimation and Inference -- 4.1.4 Checking, Effective Degrees of Freedom and Model Selection -- 4.1.5 GAM Computation with mgcv in R -- 4.1.6 Smooths of Several Predictors -- 4.1.7 Further Interpretable Structure -- 4.2 From GAM to GAMLSS: Interpretability for Model Building -- 4.2.1 GAMLSS Modelling of UK Aggregate Electricity Demand -- 4.2.1.1 Data Overview and Pre-processing -- 4.2.1.2 Interactive GAMLSS Model Building -- 4.3 From GAMs to Aggregations of Experts, Are We Still Interpretable? -- 4.3.1 Online Forecasting with Online Aggregation of Experts -- 4.3.2 Visualizing the Black Boxes -- References.
Record Nr. UNISA-996495169103316
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches / / edited by Antonio Lepore, Biagio Palumbo, Jean-Michel Poggi
Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches / / edited by Antonio Lepore, Biagio Palumbo, Jean-Michel Poggi
Edizione [1st ed. 2022.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
Descrizione fisica 1 online resource (130 pages) : illustrations
Disciplina 658.4038028563
Soggetto topico Statistics
Statistical Theory and Methods
Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
Statistics in Business, Management, Economics, Finance, Insurance
Aprenentatge automàtic
Aplicacions industrials
Soggetto genere / forma Llibres electrònics
ISBN 3-031-12402-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- 1 Different Views of Interpretability -- 1.1 Introduction -- 1.2 Interpretability: In Praise of Transparent Models -- 1.2.1 What Happened? -- 1.2.2 What Will Happen? -- 1.2.3 What Shall be Done to Make It Happen? -- 1.2.4 Patterns and Models -- 1.3 Generalizability and Interpretability with Industry 4.0 Implications -- 1.3.1 Introduction to Interpretable AI -- 1.3.2 A Wide Angle Perspective of Generalizability -- 1.3.3 Statistical Generalizability -- 1.4 Connections Between Interpretability in Machine Learning and Sensitivity Analysis of Model Outputs -- 1.4.1 Machine Learning and Uncertainty Quantification -- 1.4.2 Basics on Sensitivity Analysis and Its Main Settings -- 1.4.3 A Brief Taxonomy of Interpretability in Machine Learning -- 1.4.4 A Review of Sensitivity Analysis Powered Interpretability Methods -- References -- 2 Model Interpretability, Explainability and Trust for Manufacturing 4.0 -- 2.1 Manufacturing 4.0: Driving Trends for Data Mining -- 2.1.1 Process Monitoring in Manufacturing 4.0 -- 2.1.2 Design of Experiments in Manufacturing 4.0 -- 2.1.3 Increasing Trust in AI Models for Manufacturing 4.0: Interpretability, Explainability and Robustness -- 2.2 Additive Manufacturing as a Paradigmatic Example of Manufacturing 4.0 -- 2.3 Increase Trust in Additive Manufacturing: Robust Functional Analysis of Variance in Video-Image Analysis -- 2.3.1 The RoFANOVA Approach -- 2.3.2 An Additive Manufacturing Application -- References -- 3 Interpretability via Random Forests -- 3.1 Introduction -- 3.2 Interpretable Rule-Based Models -- 3.2.1 Literature Review -- 3.2.1.1 Definitions and Origins of Rule Models -- 3.2.1.2 Decision Trees -- 3.2.1.3 Tree-Based Rule Learning -- 3.2.1.4 Modern Rule Learning -- 3.2.2 SIRUS: Stable and Interpretable RUle Set -- 3.2.2.1 SIRUS Algorithm -- 3.2.2.2 Theoretical Analysis.
3.2.2.3 Experiments -- 3.2.3 Discussion -- 3.3 Post-Processing of Black-Box Algorithms via Variable Importance -- 3.3.1 Literature Review -- 3.3.1.1 Model-Specific Variable Importance -- 3.3.1.2 Global Sensitivity Analysis -- 3.3.1.3 Local Interpretability -- 3.3.2 Sobol-MDA -- 3.3.2.1 Sobol-MDA Algorithm -- 3.3.2.2 Sobol-MDA Properties -- 3.3.2.3 Experiments -- 3.3.3 SHAFF: SHApley eFfects Estimates via Random Forests -- 3.3.3.1 SHAFF Algorithm -- 3.3.3.2 SHAFF Consistency -- 3.3.3.3 Experiments -- 3.3.4 Discussion -- References -- 4 Interpretability in Generalized Additive Models -- 4.1 GAMs: A Basic Framework for Flexible Interpretable Regression -- 4.1.1 Flexibility Can Be Important -- 4.1.2 Making the Model Computable -- 4.1.3 Estimation and Inference -- 4.1.4 Checking, Effective Degrees of Freedom and Model Selection -- 4.1.5 GAM Computation with mgcv in R -- 4.1.6 Smooths of Several Predictors -- 4.1.7 Further Interpretable Structure -- 4.2 From GAM to GAMLSS: Interpretability for Model Building -- 4.2.1 GAMLSS Modelling of UK Aggregate Electricity Demand -- 4.2.1.1 Data Overview and Pre-processing -- 4.2.1.2 Interactive GAMLSS Model Building -- 4.3 From GAMs to Aggregations of Experts, Are We Still Interpretable? -- 4.3.1 Online Forecasting with Online Aggregation of Experts -- 4.3.2 Visualizing the Black Boxes -- References.
Record Nr. UNINA-9910619274503321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Random forests with R / / Robin Genuer, Jean-Michel Poggi
Random forests with R / / Robin Genuer, Jean-Michel Poggi
Autore Genuer Robin
Edizione [1st ed. 2020.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2020]
Descrizione fisica 1 online resource (X, 98 p. 49 illus., 5 illus. in color.)
Disciplina 519.5
Collana Use R!
Soggetto topico Mathematical statistics
R (Computer program language)
ISBN 3-030-56485-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction -- CART trees -- Random forests -- Variable importance -- Variable selection -- References.
Record Nr. UNINA-9910483571603321
Genuer Robin  
Cham, Switzerland : , : Springer, , [2020]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Random forests with R / / Robin Genuer, Jean-Michel Poggi
Random forests with R / / Robin Genuer, Jean-Michel Poggi
Autore Genuer Robin
Edizione [1st ed. 2020.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2020]
Descrizione fisica 1 online resource (X, 98 p. 49 illus., 5 illus. in color.)
Disciplina 519.5
Collana Use R!
Soggetto topico Mathematical statistics
R (Computer program language)
ISBN 3-030-56485-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction -- CART trees -- Random forests -- Variable importance -- Variable selection -- References.
Record Nr. UNISA-996418258703316
Genuer Robin  
Cham, Switzerland : , : Springer, , [2020]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Random Forests with R / / by Robin Genuer, Jean-Michel Poggi
Random Forests with R / / by Robin Genuer, Jean-Michel Poggi
Autore Genuer Robin
Edizione [1st ed. 2020.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020
Descrizione fisica 1 online resource (X, 98 p. 49 illus., 5 illus. in color.)
Disciplina 519.5
Collana Use R!
Soggetto topico Statistics
Big data
Bioinformatics
Biometry
Social sciences - Statistical methods
Statistical Theory and Methods
Big Data
Biostatistics
Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy
ISBN 9783030564858
3030564851
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction -- CART trees -- Random forests -- Variable importance -- Variable selection -- References.
Record Nr. UNINA-9910863167703321
Genuer Robin  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui