Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Milton
CRC Press LLC
2021
|
| Schriftenreihe: | Chapman and Hall/CRC Data Science Ser
|
| Schlagworte: | |
| Online-Zugang: | HWR01 |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 online resource (327 pages) |
| ISBN: | 9780429651373 |
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| 505 | 8 | |a Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Part I Introduction -- 1 Introduction -- 1.1 The aim of the book -- 1.2 A bit of philosophy: three laws of model explanation -- 1.3 Terminology -- 1.4 Black-box models and glass-box models -- 1.5 Model-agnostic and model-specific approach -- 1.6 The structure of the book -- 1.7 What is included in this book and what is not -- 1.8 Acknowledgements -- 2 Model Development -- 2.1 Introduction -- 2.2 Model-development process -- 2.3 Notation -- 2.4 Data understanding -- 2.5 Model assembly (fitting) -- 2.6 Model audit -- 3 Do-it-yourself -- 3.1 Do-it-yourself with R -- 3.1.1 What to install? -- 3.1.2 How to work with DALEX? -- 3.1.3 How to work with archivist? -- 3.2 Do-it-yourself with Python -- 3.2.1 What to install? -- 3.2.2 How to work with dalex? -- 3.2.3 Code snippets for Python -- 4 Datasets and Models -- 4.1 Sinking of the RMS Titanic -- 4.1.1 Data exploration -- 4.2 Models for RMS Titanic, snippets for R -- 4.2.1 Logistic regression model -- 4.2.2 Random forest model -- 4.2.3 Gradient boosting model -- 4.2.4 Support vector machine model -- 4.2.5 Models' predictions -- 4.2.6 Models' explainers -- 4.2.7 List of model-objects -- 4.3 Models for RMS Titanic, snippets for Python -- 4.3.1 Logistic regression model -- 4.3.2 Random forest model -- 4.3.3 Gradient boosting model -- 4.3.4 Support vector machine model -- 4.3.5 Models' predictions -- 4.3.6 Models' explainers -- 4.4 Apartment prices -- 4.4.1 Data exploration -- 4.5 Models for apartment prices, snippets for R -- 4.5.1 Linear regression model -- 4.5.2 Random forest model -- 4.5.3 Support vector machine model -- 4.5.4 Models' predictions -- 4.5.5 Models' explainers -- 4.5.6 List of model-objects -- 4.6 Models for apartment prices, snippets for Python -- 4.6.1 Linear regression model | |
| 505 | 8 | |a 4.6.2 Random forest model -- 4.6.3 Support vector machine model -- 4.6.4 Models' predictions -- 4.6.5 Models' explainers -- Part II Instance Level -- 5 Introduction to Instance-level Exploration -- 6 Break-down Plots for Additive Attributions -- 6.1 Introduction -- 6.2 Intuition -- 6.3 Method -- 6.3.1 Break-down for linear models -- 6.3.2 Break-down for a general case -- 6.4 Example: Titanic data -- 6.5 Pros and cons -- 6.6 Code snippets for R -- 6.6.1 Basic use of the predict_parts() function -- 6.6.2 Advanced use of the predict_parts() function -- 6.7 Code snippets for Python -- 7 Break-down Plots for Interactions -- 7.1 Intuition -- 7.2 Method -- 7.3 Example: Titanic data -- 7.4 Pros and cons -- 7.5 Code snippets for R -- 7.6 Code snippets for Python -- 8 Shapley Additive Explanations (SHAP) for Average Attributions -- 8.1 Intuition -- 8.2 Method -- 8.3 Example: Titanic data -- 8.4 Pros and cons -- 8.5 Code snippets for R -- 8.6 Code snippets for Python -- 9 Local Interpretable Model-agnostic Explanations (LIME) -- 9.1 Introduction -- 9.2 Intuition -- 9.3 Method -- 9.3.1 Interpretable data representation -- 9.3.2 Sampling around the instance of interest -- 9.3.3 Fitting the glass-box model -- 9.4 Example: Titanic data -- 9.5 Pros and cons -- 9.6 Code snippets for R -- 9.6.1 The lime package -- 9.6.2 The localModel package -- 9.6.3 The iml package -- 9.7 Code snippets for Python -- 10 Ceteris-paribus Profiles -- 10.1 Introduction -- 10.2 Intuition -- 10.3 Method -- 10.4 Example: Titanic data -- 10.5 Pros and cons -- 10.6 Code snippets for R -- 10.6.1 Basic use of the predict_profile() function -- 10.6.2 Advanced use of the predict_profile() function -- 10.6.3 Comparison of models (champion-challenger) -- 10.7 Code snippets for Python -- 11 Ceteris-paribus Oscillations -- 11.1 Introduction -- 11.2 Intuition -- 11.3 Method | |
| 505 | 8 | |a 11.4 Example: Titanic data -- 11.5 Pros and cons -- 11.6 Code snippets for R -- 11.6.1 Basic use of the predict_parts() function -- 11.6.2 Advanced use of the predict_parts() function -- 11.7 Code snippets for Python -- 12 Local-diagnostics Plots -- 12.1 Introduction -- 12.2 Intuition -- 12.3 Method -- 12.3.1 Nearest neighbors -- 12.3.2 Local-fidelity plot -- 12.3.3 Local-stability plot -- 12.4 Example: Titanic -- 12.5 Pros and cons -- 12.6 Code snippets for R -- 12.7 Code snippets for Python -- 13 Summary of Instance-level Exploration -- 13.1 Introduction -- 13.2 Number of explanatory variables in the model -- 13.2.1 Low to medium number of explanatory variables -- 13.2.2 Medium to a large number of explanatory variables -- 13.2.3 Very large number of explanatory variables -- 13.3 Correlated explanatory variables -- 13.4 Models with interactions -- 13.5 Sparse explanations -- 13.6 Additional uses of model exploration and explanation -- 13.7 Comparison of models (champion-challenger analysis) -- Part III Dataset Level -- 14 Introduction to Dataset-level Exploration -- 15 Model-performance Measures -- 15.1 Introduction -- 15.2 Intuition -- 15.3 Method -- 15.3.1 Continuous dependent variable -- 15.3.1.1 Goodness-of-fit -- 15.3.1.2 Goodness-of-prediction -- 15.3.2 Binary dependent variable -- 15.3.2.1 Goodness-of-fit -- 15.3.2.2 Goodness-of-prediction -- 15.3.3 Categorical dependent variable -- 15.3.3.1 Goodness-of-fit -- 15.3.3.2 Goodness-of-prediction -- 15.3.4 Count dependent variable -- 15.4 Example -- 15.4.1 Apartment prices -- 15.4.2 Titanic data -- 15.5 Pros and cons -- 15.6 Code snippets for R -- 15.7 Code snippets for Python -- 16 Variable-importance Measures -- 16.1 Introduction -- 16.2 Intuition -- 16.3 Method -- 16.4 Example: Titanic data -- 16.5 Pros and cons -- 16.6 Code snippets for R -- 16.7 Code snippets for Python | |
| 505 | 8 | |a 17 Partial-dependence Profiles -- 17.1 Introduction -- 17.2 Intuition -- 17.3 Method -- 17.3.1 Partial-dependence profiles -- 17.3.2 Clustered partial-dependence profiles -- 17.3.3 Grouped partial-dependence profiles -- 17.3.4 Contrastive partial-dependence profiles -- 17.4 Example: apartment-prices data -- 17.4.1 Partial-dependence profiles -- 17.4.2 Clustered partial-dependence profiles -- 17.4.3 Grouped partial-dependence profiles -- 17.4.4 Contrastive partial-dependence profiles -- 17.5 Pros and cons -- 17.6 Code snippets for R -- 17.6.1 Partial-dependence profiles -- 17.6.2 Clustered partial-dependence profiles -- 17.6.3 Grouped partial-dependence profiles -- 17.6.4 Contrastive partial-dependence profiles -- 17.7 Code snippets for Python -- 17.7.1 Grouped partial-dependence profiles -- 17.7.2 Contrastive partial-dependence profiles -- 18 Local-dependence and Accumulated-local Profiles -- 18.1 Introduction -- 18.2 Intuition -- 18.3 Method -- 18.3.1 Local-dependence profile -- 18.3.2 Accumulated-local profile -- 18.3.3 Dependence profiles for a model with interaction and correlated explanatory variables: an example -- 18.4 Example: apartment-prices data -- 18.5 Pros and cons -- 18.6 Code snippets for R -- 18.7 Code snippets for Python -- 19 Residual-diagnostics Plots -- 19.1 Introduction -- 19.2 Intuition -- 19.3 Method -- 19.4 Example: apartment-prices data -- 19.5 Pros and cons -- 19.6 Code snippets for R -- 19.7 Code snippets for Python -- 20 Summary of Dataset-level Exploration -- 20.1 Introduction -- 20.2 Exploration on training/testing data -- 20.3 Correlated explanatory variables -- 20.4 Comparison of models (champion-challenger analysis) -- Part IV Use-cases -- 21 FIFA 19 -- 21.1 Introduction -- 21.2 Data preparation -- 21.2.1 Code snippets for R -- 21.2.2 Code snippets for Python -- 21.3 Data understanding -- 21.4 Model assembly | |
| 505 | 8 | |a 21.4.1 Code snippets for R -- 21.4.2 Code snippets for Python -- 21.5 Model audit -- 21.5.1 Code snippets for R -- 21.5.2 Code snippets for Python -- 21.6 Model understanding (dataset-level explanations) -- 21.6.1 Code snippets for R -- 21.6.2 Code snippets for Python -- 21.7 Instance-level explanations -- 21.7.1 Robert Lewandowski -- 21.7.2 Code snippets for R -- 21.7.3 Code snippets for Python -- 21.7.4 CR7 -- 21.7.5 Wojciech Szczęsny -- 21.7.6 Lionel Messi -- 22 Reproducibility -- 22.1 Package versions for R -- 22.2 Package versions for Python -- Bibliography -- Index | |
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Datensatz im Suchindex
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| author | Biecek, Przemyslaw |
| author_facet | Biecek, Przemyslaw |
| author_role | aut |
| author_sort | Biecek, Przemyslaw |
| author_variant | p b pb |
| building | Verbundindex |
| bvnumber | BV047688960 |
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| contents | Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Part I Introduction -- 1 Introduction -- 1.1 The aim of the book -- 1.2 A bit of philosophy: three laws of model explanation -- 1.3 Terminology -- 1.4 Black-box models and glass-box models -- 1.5 Model-agnostic and model-specific approach -- 1.6 The structure of the book -- 1.7 What is included in this book and what is not -- 1.8 Acknowledgements -- 2 Model Development -- 2.1 Introduction -- 2.2 Model-development process -- 2.3 Notation -- 2.4 Data understanding -- 2.5 Model assembly (fitting) -- 2.6 Model audit -- 3 Do-it-yourself -- 3.1 Do-it-yourself with R -- 3.1.1 What to install? -- 3.1.2 How to work with DALEX? -- 3.1.3 How to work with archivist? -- 3.2 Do-it-yourself with Python -- 3.2.1 What to install? -- 3.2.2 How to work with dalex? -- 3.2.3 Code snippets for Python -- 4 Datasets and Models -- 4.1 Sinking of the RMS Titanic -- 4.1.1 Data exploration -- 4.2 Models for RMS Titanic, snippets for R -- 4.2.1 Logistic regression model -- 4.2.2 Random forest model -- 4.2.3 Gradient boosting model -- 4.2.4 Support vector machine model -- 4.2.5 Models' predictions -- 4.2.6 Models' explainers -- 4.2.7 List of model-objects -- 4.3 Models for RMS Titanic, snippets for Python -- 4.3.1 Logistic regression model -- 4.3.2 Random forest model -- 4.3.3 Gradient boosting model -- 4.3.4 Support vector machine model -- 4.3.5 Models' predictions -- 4.3.6 Models' explainers -- 4.4 Apartment prices -- 4.4.1 Data exploration -- 4.5 Models for apartment prices, snippets for R -- 4.5.1 Linear regression model -- 4.5.2 Random forest model -- 4.5.3 Support vector machine model -- 4.5.4 Models' predictions -- 4.5.5 Models' explainers -- 4.5.6 List of model-objects -- 4.6 Models for apartment prices, snippets for Python -- 4.6.1 Linear regression model 4.6.2 Random forest model -- 4.6.3 Support vector machine model -- 4.6.4 Models' predictions -- 4.6.5 Models' explainers -- Part II Instance Level -- 5 Introduction to Instance-level Exploration -- 6 Break-down Plots for Additive Attributions -- 6.1 Introduction -- 6.2 Intuition -- 6.3 Method -- 6.3.1 Break-down for linear models -- 6.3.2 Break-down for a general case -- 6.4 Example: Titanic data -- 6.5 Pros and cons -- 6.6 Code snippets for R -- 6.6.1 Basic use of the predict_parts() function -- 6.6.2 Advanced use of the predict_parts() function -- 6.7 Code snippets for Python -- 7 Break-down Plots for Interactions -- 7.1 Intuition -- 7.2 Method -- 7.3 Example: Titanic data -- 7.4 Pros and cons -- 7.5 Code snippets for R -- 7.6 Code snippets for Python -- 8 Shapley Additive Explanations (SHAP) for Average Attributions -- 8.1 Intuition -- 8.2 Method -- 8.3 Example: Titanic data -- 8.4 Pros and cons -- 8.5 Code snippets for R -- 8.6 Code snippets for Python -- 9 Local Interpretable Model-agnostic Explanations (LIME) -- 9.1 Introduction -- 9.2 Intuition -- 9.3 Method -- 9.3.1 Interpretable data representation -- 9.3.2 Sampling around the instance of interest -- 9.3.3 Fitting the glass-box model -- 9.4 Example: Titanic data -- 9.5 Pros and cons -- 9.6 Code snippets for R -- 9.6.1 The lime package -- 9.6.2 The localModel package -- 9.6.3 The iml package -- 9.7 Code snippets for Python -- 10 Ceteris-paribus Profiles -- 10.1 Introduction -- 10.2 Intuition -- 10.3 Method -- 10.4 Example: Titanic data -- 10.5 Pros and cons -- 10.6 Code snippets for R -- 10.6.1 Basic use of the predict_profile() function -- 10.6.2 Advanced use of the predict_profile() function -- 10.6.3 Comparison of models (champion-challenger) -- 10.7 Code snippets for Python -- 11 Ceteris-paribus Oscillations -- 11.1 Introduction -- 11.2 Intuition -- 11.3 Method 11.4 Example: Titanic data -- 11.5 Pros and cons -- 11.6 Code snippets for R -- 11.6.1 Basic use of the predict_parts() function -- 11.6.2 Advanced use of the predict_parts() function -- 11.7 Code snippets for Python -- 12 Local-diagnostics Plots -- 12.1 Introduction -- 12.2 Intuition -- 12.3 Method -- 12.3.1 Nearest neighbors -- 12.3.2 Local-fidelity plot -- 12.3.3 Local-stability plot -- 12.4 Example: Titanic -- 12.5 Pros and cons -- 12.6 Code snippets for R -- 12.7 Code snippets for Python -- 13 Summary of Instance-level Exploration -- 13.1 Introduction -- 13.2 Number of explanatory variables in the model -- 13.2.1 Low to medium number of explanatory variables -- 13.2.2 Medium to a large number of explanatory variables -- 13.2.3 Very large number of explanatory variables -- 13.3 Correlated explanatory variables -- 13.4 Models with interactions -- 13.5 Sparse explanations -- 13.6 Additional uses of model exploration and explanation -- 13.7 Comparison of models (champion-challenger analysis) -- Part III Dataset Level -- 14 Introduction to Dataset-level Exploration -- 15 Model-performance Measures -- 15.1 Introduction -- 15.2 Intuition -- 15.3 Method -- 15.3.1 Continuous dependent variable -- 15.3.1.1 Goodness-of-fit -- 15.3.1.2 Goodness-of-prediction -- 15.3.2 Binary dependent variable -- 15.3.2.1 Goodness-of-fit -- 15.3.2.2 Goodness-of-prediction -- 15.3.3 Categorical dependent variable -- 15.3.3.1 Goodness-of-fit -- 15.3.3.2 Goodness-of-prediction -- 15.3.4 Count dependent variable -- 15.4 Example -- 15.4.1 Apartment prices -- 15.4.2 Titanic data -- 15.5 Pros and cons -- 15.6 Code snippets for R -- 15.7 Code snippets for Python -- 16 Variable-importance Measures -- 16.1 Introduction -- 16.2 Intuition -- 16.3 Method -- 16.4 Example: Titanic data -- 16.5 Pros and cons -- 16.6 Code snippets for R -- 16.7 Code snippets for Python 17 Partial-dependence Profiles -- 17.1 Introduction -- 17.2 Intuition -- 17.3 Method -- 17.3.1 Partial-dependence profiles -- 17.3.2 Clustered partial-dependence profiles -- 17.3.3 Grouped partial-dependence profiles -- 17.3.4 Contrastive partial-dependence profiles -- 17.4 Example: apartment-prices data -- 17.4.1 Partial-dependence profiles -- 17.4.2 Clustered partial-dependence profiles -- 17.4.3 Grouped partial-dependence profiles -- 17.4.4 Contrastive partial-dependence profiles -- 17.5 Pros and cons -- 17.6 Code snippets for R -- 17.6.1 Partial-dependence profiles -- 17.6.2 Clustered partial-dependence profiles -- 17.6.3 Grouped partial-dependence profiles -- 17.6.4 Contrastive partial-dependence profiles -- 17.7 Code snippets for Python -- 17.7.1 Grouped partial-dependence profiles -- 17.7.2 Contrastive partial-dependence profiles -- 18 Local-dependence and Accumulated-local Profiles -- 18.1 Introduction -- 18.2 Intuition -- 18.3 Method -- 18.3.1 Local-dependence profile -- 18.3.2 Accumulated-local profile -- 18.3.3 Dependence profiles for a model with interaction and correlated explanatory variables: an example -- 18.4 Example: apartment-prices data -- 18.5 Pros and cons -- 18.6 Code snippets for R -- 18.7 Code snippets for Python -- 19 Residual-diagnostics Plots -- 19.1 Introduction -- 19.2 Intuition -- 19.3 Method -- 19.4 Example: apartment-prices data -- 19.5 Pros and cons -- 19.6 Code snippets for R -- 19.7 Code snippets for Python -- 20 Summary of Dataset-level Exploration -- 20.1 Introduction -- 20.2 Exploration on training/testing data -- 20.3 Correlated explanatory variables -- 20.4 Comparison of models (champion-challenger analysis) -- Part IV Use-cases -- 21 FIFA 19 -- 21.1 Introduction -- 21.2 Data preparation -- 21.2.1 Code snippets for R -- 21.2.2 Code snippets for Python -- 21.3 Data understanding -- 21.4 Model assembly 21.4.1 Code snippets for R -- 21.4.2 Code snippets for Python -- 21.5 Model audit -- 21.5.1 Code snippets for R -- 21.5.2 Code snippets for Python -- 21.6 Model understanding (dataset-level explanations) -- 21.6.1 Code snippets for R -- 21.6.2 Code snippets for Python -- 21.7 Instance-level explanations -- 21.7.1 Robert Lewandowski -- 21.7.2 Code snippets for R -- 21.7.3 Code snippets for Python -- 21.7.4 CR7 -- 21.7.5 Wojciech Szczęsny -- 21.7.6 Lionel Messi -- 22 Reproducibility -- 22.1 Package versions for R -- 22.2 Package versions for Python -- Bibliography -- Index |
| ctrlnum | (ZDB-30-PQE)EBC6451194 (ZDB-30-PAD)EBC6451194 (ZDB-89-EBL)EBL6451194 (OCoLC)1229929493 (DE-599)BVBBV047688960 |
| discipline | Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Mathematik Wirtschaftswissenschaften |
| format | Electronic eBook |
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tag="490" ind1="0" ind2=" "><subfield code="a">Chapman and Hall/CRC Data Science Ser</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Description based on publisher supplied metadata and other sources</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Part I Introduction -- 1 Introduction -- 1.1 The aim of the book -- 1.2 A bit of philosophy: three laws of model explanation -- 1.3 Terminology -- 1.4 Black-box models and glass-box models -- 1.5 Model-agnostic and model-specific approach -- 1.6 The structure of the book -- 1.7 What is included in this book and what is not -- 1.8 Acknowledgements -- 2 Model Development -- 2.1 Introduction -- 2.2 Model-development process -- 2.3 Notation -- 2.4 Data understanding -- 2.5 Model assembly (fitting) -- 2.6 Model audit -- 3 Do-it-yourself -- 3.1 Do-it-yourself with R -- 3.1.1 What to install? -- 3.1.2 How to work with DALEX? -- 3.1.3 How to work with archivist? -- 3.2 Do-it-yourself with Python -- 3.2.1 What to install? -- 3.2.2 How to work with dalex? -- 3.2.3 Code snippets for Python -- 4 Datasets and Models -- 4.1 Sinking of the RMS Titanic -- 4.1.1 Data exploration -- 4.2 Models for RMS Titanic, snippets for R -- 4.2.1 Logistic regression model -- 4.2.2 Random forest model -- 4.2.3 Gradient boosting model -- 4.2.4 Support vector machine model -- 4.2.5 Models' predictions -- 4.2.6 Models' explainers -- 4.2.7 List of model-objects -- 4.3 Models for RMS Titanic, snippets for Python -- 4.3.1 Logistic regression model -- 4.3.2 Random forest model -- 4.3.3 Gradient boosting model -- 4.3.4 Support vector machine model -- 4.3.5 Models' predictions -- 4.3.6 Models' explainers -- 4.4 Apartment prices -- 4.4.1 Data exploration -- 4.5 Models for apartment prices, snippets for R -- 4.5.1 Linear regression model -- 4.5.2 Random forest model -- 4.5.3 Support vector machine model -- 4.5.4 Models' predictions -- 4.5.5 Models' explainers -- 4.5.6 List of model-objects -- 4.6 Models for apartment prices, snippets for Python -- 4.6.1 Linear regression model</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">4.6.2 Random forest model -- 4.6.3 Support vector machine model -- 4.6.4 Models' predictions -- 4.6.5 Models' explainers -- Part II Instance Level -- 5 Introduction to Instance-level Exploration -- 6 Break-down Plots for Additive Attributions -- 6.1 Introduction -- 6.2 Intuition -- 6.3 Method -- 6.3.1 Break-down for linear models -- 6.3.2 Break-down for a general case -- 6.4 Example: Titanic data -- 6.5 Pros and cons -- 6.6 Code snippets for R -- 6.6.1 Basic use of the predict_parts() function -- 6.6.2 Advanced use of the predict_parts() function -- 6.7 Code snippets for Python -- 7 Break-down Plots for Interactions -- 7.1 Intuition -- 7.2 Method -- 7.3 Example: Titanic data -- 7.4 Pros and cons -- 7.5 Code snippets for R -- 7.6 Code snippets for Python -- 8 Shapley Additive Explanations (SHAP) for Average Attributions -- 8.1 Intuition -- 8.2 Method -- 8.3 Example: Titanic data -- 8.4 Pros and cons -- 8.5 Code snippets for R -- 8.6 Code snippets for Python -- 9 Local Interpretable Model-agnostic Explanations (LIME) -- 9.1 Introduction -- 9.2 Intuition -- 9.3 Method -- 9.3.1 Interpretable data representation -- 9.3.2 Sampling around the instance of interest -- 9.3.3 Fitting the glass-box model -- 9.4 Example: Titanic data -- 9.5 Pros and cons -- 9.6 Code snippets for R -- 9.6.1 The lime package -- 9.6.2 The localModel package -- 9.6.3 The iml package -- 9.7 Code snippets for Python -- 10 Ceteris-paribus Profiles -- 10.1 Introduction -- 10.2 Intuition -- 10.3 Method -- 10.4 Example: Titanic data -- 10.5 Pros and cons -- 10.6 Code snippets for R -- 10.6.1 Basic use of the predict_profile() function -- 10.6.2 Advanced use of the predict_profile() function -- 10.6.3 Comparison of models (champion-challenger) -- 10.7 Code snippets for Python -- 11 Ceteris-paribus Oscillations -- 11.1 Introduction -- 11.2 Intuition -- 11.3 Method</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">11.4 Example: Titanic data -- 11.5 Pros and cons -- 11.6 Code snippets for R -- 11.6.1 Basic use of the predict_parts() function -- 11.6.2 Advanced use of the predict_parts() function -- 11.7 Code snippets for Python -- 12 Local-diagnostics Plots -- 12.1 Introduction -- 12.2 Intuition -- 12.3 Method -- 12.3.1 Nearest neighbors -- 12.3.2 Local-fidelity plot -- 12.3.3 Local-stability plot -- 12.4 Example: Titanic -- 12.5 Pros and cons -- 12.6 Code snippets for R -- 12.7 Code snippets for Python -- 13 Summary of Instance-level Exploration -- 13.1 Introduction -- 13.2 Number of explanatory variables in the model -- 13.2.1 Low to medium number of explanatory variables -- 13.2.2 Medium to a large number of explanatory variables -- 13.2.3 Very large number of explanatory variables -- 13.3 Correlated explanatory variables -- 13.4 Models with interactions -- 13.5 Sparse explanations -- 13.6 Additional uses of model exploration and explanation -- 13.7 Comparison of models (champion-challenger analysis) -- Part III Dataset Level -- 14 Introduction to Dataset-level Exploration -- 15 Model-performance Measures -- 15.1 Introduction -- 15.2 Intuition -- 15.3 Method -- 15.3.1 Continuous dependent variable -- 15.3.1.1 Goodness-of-fit -- 15.3.1.2 Goodness-of-prediction -- 15.3.2 Binary dependent variable -- 15.3.2.1 Goodness-of-fit -- 15.3.2.2 Goodness-of-prediction -- 15.3.3 Categorical dependent variable -- 15.3.3.1 Goodness-of-fit -- 15.3.3.2 Goodness-of-prediction -- 15.3.4 Count dependent variable -- 15.4 Example -- 15.4.1 Apartment prices -- 15.4.2 Titanic data -- 15.5 Pros and cons -- 15.6 Code snippets for R -- 15.7 Code snippets for Python -- 16 Variable-importance Measures -- 16.1 Introduction -- 16.2 Intuition -- 16.3 Method -- 16.4 Example: Titanic data -- 16.5 Pros and cons -- 16.6 Code snippets for R -- 16.7 Code snippets for Python</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">17 Partial-dependence Profiles -- 17.1 Introduction -- 17.2 Intuition -- 17.3 Method -- 17.3.1 Partial-dependence profiles -- 17.3.2 Clustered partial-dependence profiles -- 17.3.3 Grouped partial-dependence profiles -- 17.3.4 Contrastive partial-dependence profiles -- 17.4 Example: apartment-prices data -- 17.4.1 Partial-dependence profiles -- 17.4.2 Clustered partial-dependence profiles -- 17.4.3 Grouped partial-dependence profiles -- 17.4.4 Contrastive partial-dependence profiles -- 17.5 Pros and cons -- 17.6 Code snippets for R -- 17.6.1 Partial-dependence profiles -- 17.6.2 Clustered partial-dependence profiles -- 17.6.3 Grouped partial-dependence profiles -- 17.6.4 Contrastive partial-dependence profiles -- 17.7 Code snippets for Python -- 17.7.1 Grouped partial-dependence profiles -- 17.7.2 Contrastive partial-dependence profiles -- 18 Local-dependence and Accumulated-local Profiles -- 18.1 Introduction -- 18.2 Intuition -- 18.3 Method -- 18.3.1 Local-dependence profile -- 18.3.2 Accumulated-local profile -- 18.3.3 Dependence profiles for a model with interaction and correlated explanatory variables: an example -- 18.4 Example: apartment-prices data -- 18.5 Pros and cons -- 18.6 Code snippets for R -- 18.7 Code snippets for Python -- 19 Residual-diagnostics Plots -- 19.1 Introduction -- 19.2 Intuition -- 19.3 Method -- 19.4 Example: apartment-prices data -- 19.5 Pros and cons -- 19.6 Code snippets for R -- 19.7 Code snippets for Python -- 20 Summary of Dataset-level Exploration -- 20.1 Introduction -- 20.2 Exploration on training/testing data -- 20.3 Correlated explanatory variables -- 20.4 Comparison of models (champion-challenger analysis) -- Part IV Use-cases -- 21 FIFA 19 -- 21.1 Introduction -- 21.2 Data preparation -- 21.2.1 Code snippets for R -- 21.2.2 Code snippets for Python -- 21.3 Data understanding -- 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| id | DE-604.BV047688960 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T18:57:01Z |
| indexdate | 2024-07-10T09:19:15Z |
| institution | BVB |
| isbn | 9780429651373 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033072975 |
| oclc_num | 1229929493 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 online resource (327 pages) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | CRC Press LLC |
| record_format | marc |
| series2 | Chapman and Hall/CRC Data Science Ser |
| spelling | Biecek, Przemyslaw Verfasser aut Explanatory Model Analysis Explore, Explain, and Examine Predictive Models Milton CRC Press LLC 2021 ©2021 1 online resource (327 pages) txt rdacontent c rdamedia cr rdacarrier Chapman and Hall/CRC Data Science Ser Description based on publisher supplied metadata and other sources Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Part I Introduction -- 1 Introduction -- 1.1 The aim of the book -- 1.2 A bit of philosophy: three laws of model explanation -- 1.3 Terminology -- 1.4 Black-box models and glass-box models -- 1.5 Model-agnostic and model-specific approach -- 1.6 The structure of the book -- 1.7 What is included in this book and what is not -- 1.8 Acknowledgements -- 2 Model Development -- 2.1 Introduction -- 2.2 Model-development process -- 2.3 Notation -- 2.4 Data understanding -- 2.5 Model assembly (fitting) -- 2.6 Model audit -- 3 Do-it-yourself -- 3.1 Do-it-yourself with R -- 3.1.1 What to install? -- 3.1.2 How to work with DALEX? -- 3.1.3 How to work with archivist? -- 3.2 Do-it-yourself with Python -- 3.2.1 What to install? -- 3.2.2 How to work with dalex? -- 3.2.3 Code snippets for Python -- 4 Datasets and Models -- 4.1 Sinking of the RMS Titanic -- 4.1.1 Data exploration -- 4.2 Models for RMS Titanic, snippets for R -- 4.2.1 Logistic regression model -- 4.2.2 Random forest model -- 4.2.3 Gradient boosting model -- 4.2.4 Support vector machine model -- 4.2.5 Models' predictions -- 4.2.6 Models' explainers -- 4.2.7 List of model-objects -- 4.3 Models for RMS Titanic, snippets for Python -- 4.3.1 Logistic regression model -- 4.3.2 Random forest model -- 4.3.3 Gradient boosting model -- 4.3.4 Support vector machine model -- 4.3.5 Models' predictions -- 4.3.6 Models' explainers -- 4.4 Apartment prices -- 4.4.1 Data exploration -- 4.5 Models for apartment prices, snippets for R -- 4.5.1 Linear regression model -- 4.5.2 Random forest model -- 4.5.3 Support vector machine model -- 4.5.4 Models' predictions -- 4.5.5 Models' explainers -- 4.5.6 List of model-objects -- 4.6 Models for apartment prices, snippets for Python -- 4.6.1 Linear regression model 4.6.2 Random forest model -- 4.6.3 Support vector machine model -- 4.6.4 Models' predictions -- 4.6.5 Models' explainers -- Part II Instance Level -- 5 Introduction to Instance-level Exploration -- 6 Break-down Plots for Additive Attributions -- 6.1 Introduction -- 6.2 Intuition -- 6.3 Method -- 6.3.1 Break-down for linear models -- 6.3.2 Break-down for a general case -- 6.4 Example: Titanic data -- 6.5 Pros and cons -- 6.6 Code snippets for R -- 6.6.1 Basic use of the predict_parts() function -- 6.6.2 Advanced use of the predict_parts() function -- 6.7 Code snippets for Python -- 7 Break-down Plots for Interactions -- 7.1 Intuition -- 7.2 Method -- 7.3 Example: Titanic data -- 7.4 Pros and cons -- 7.5 Code snippets for R -- 7.6 Code snippets for Python -- 8 Shapley Additive Explanations (SHAP) for Average Attributions -- 8.1 Intuition -- 8.2 Method -- 8.3 Example: Titanic data -- 8.4 Pros and cons -- 8.5 Code snippets for R -- 8.6 Code snippets for Python -- 9 Local Interpretable Model-agnostic Explanations (LIME) -- 9.1 Introduction -- 9.2 Intuition -- 9.3 Method -- 9.3.1 Interpretable data representation -- 9.3.2 Sampling around the instance of interest -- 9.3.3 Fitting the glass-box model -- 9.4 Example: Titanic data -- 9.5 Pros and cons -- 9.6 Code snippets for R -- 9.6.1 The lime package -- 9.6.2 The localModel package -- 9.6.3 The iml package -- 9.7 Code snippets for Python -- 10 Ceteris-paribus Profiles -- 10.1 Introduction -- 10.2 Intuition -- 10.3 Method -- 10.4 Example: Titanic data -- 10.5 Pros and cons -- 10.6 Code snippets for R -- 10.6.1 Basic use of the predict_profile() function -- 10.6.2 Advanced use of the predict_profile() function -- 10.6.3 Comparison of models (champion-challenger) -- 10.7 Code snippets for Python -- 11 Ceteris-paribus Oscillations -- 11.1 Introduction -- 11.2 Intuition -- 11.3 Method 11.4 Example: Titanic data -- 11.5 Pros and cons -- 11.6 Code snippets for R -- 11.6.1 Basic use of the predict_parts() function -- 11.6.2 Advanced use of the predict_parts() function -- 11.7 Code snippets for Python -- 12 Local-diagnostics Plots -- 12.1 Introduction -- 12.2 Intuition -- 12.3 Method -- 12.3.1 Nearest neighbors -- 12.3.2 Local-fidelity plot -- 12.3.3 Local-stability plot -- 12.4 Example: Titanic -- 12.5 Pros and cons -- 12.6 Code snippets for R -- 12.7 Code snippets for Python -- 13 Summary of Instance-level Exploration -- 13.1 Introduction -- 13.2 Number of explanatory variables in the model -- 13.2.1 Low to medium number of explanatory variables -- 13.2.2 Medium to a large number of explanatory variables -- 13.2.3 Very large number of explanatory variables -- 13.3 Correlated explanatory variables -- 13.4 Models with interactions -- 13.5 Sparse explanations -- 13.6 Additional uses of model exploration and explanation -- 13.7 Comparison of models (champion-challenger analysis) -- Part III Dataset Level -- 14 Introduction to Dataset-level Exploration -- 15 Model-performance Measures -- 15.1 Introduction -- 15.2 Intuition -- 15.3 Method -- 15.3.1 Continuous dependent variable -- 15.3.1.1 Goodness-of-fit -- 15.3.1.2 Goodness-of-prediction -- 15.3.2 Binary dependent variable -- 15.3.2.1 Goodness-of-fit -- 15.3.2.2 Goodness-of-prediction -- 15.3.3 Categorical dependent variable -- 15.3.3.1 Goodness-of-fit -- 15.3.3.2 Goodness-of-prediction -- 15.3.4 Count dependent variable -- 15.4 Example -- 15.4.1 Apartment prices -- 15.4.2 Titanic data -- 15.5 Pros and cons -- 15.6 Code snippets for R -- 15.7 Code snippets for Python -- 16 Variable-importance Measures -- 16.1 Introduction -- 16.2 Intuition -- 16.3 Method -- 16.4 Example: Titanic data -- 16.5 Pros and cons -- 16.6 Code snippets for R -- 16.7 Code snippets for Python 17 Partial-dependence Profiles -- 17.1 Introduction -- 17.2 Intuition -- 17.3 Method -- 17.3.1 Partial-dependence profiles -- 17.3.2 Clustered partial-dependence profiles -- 17.3.3 Grouped partial-dependence profiles -- 17.3.4 Contrastive partial-dependence profiles -- 17.4 Example: apartment-prices data -- 17.4.1 Partial-dependence profiles -- 17.4.2 Clustered partial-dependence profiles -- 17.4.3 Grouped partial-dependence profiles -- 17.4.4 Contrastive partial-dependence profiles -- 17.5 Pros and cons -- 17.6 Code snippets for R -- 17.6.1 Partial-dependence profiles -- 17.6.2 Clustered partial-dependence profiles -- 17.6.3 Grouped partial-dependence profiles -- 17.6.4 Contrastive partial-dependence profiles -- 17.7 Code snippets for Python -- 17.7.1 Grouped partial-dependence profiles -- 17.7.2 Contrastive partial-dependence profiles -- 18 Local-dependence and Accumulated-local Profiles -- 18.1 Introduction -- 18.2 Intuition -- 18.3 Method -- 18.3.1 Local-dependence profile -- 18.3.2 Accumulated-local profile -- 18.3.3 Dependence profiles for a model with interaction and correlated explanatory variables: an example -- 18.4 Example: apartment-prices data -- 18.5 Pros and cons -- 18.6 Code snippets for R -- 18.7 Code snippets for Python -- 19 Residual-diagnostics Plots -- 19.1 Introduction -- 19.2 Intuition -- 19.3 Method -- 19.4 Example: apartment-prices data -- 19.5 Pros and cons -- 19.6 Code snippets for R -- 19.7 Code snippets for Python -- 20 Summary of Dataset-level Exploration -- 20.1 Introduction -- 20.2 Exploration on training/testing data -- 20.3 Correlated explanatory variables -- 20.4 Comparison of models (champion-challenger analysis) -- Part IV Use-cases -- 21 FIFA 19 -- 21.1 Introduction -- 21.2 Data preparation -- 21.2.1 Code snippets for R -- 21.2.2 Code snippets for Python -- 21.3 Data understanding -- 21.4 Model assembly 21.4.1 Code snippets for R -- 21.4.2 Code snippets for Python -- 21.5 Model audit -- 21.5.1 Code snippets for R -- 21.5.2 Code snippets for Python -- 21.6 Model understanding (dataset-level explanations) -- 21.6.1 Code snippets for R -- 21.6.2 Code snippets for Python -- 21.7 Instance-level explanations -- 21.7.1 Robert Lewandowski -- 21.7.2 Code snippets for R -- 21.7.3 Code snippets for Python -- 21.7.4 CR7 -- 21.7.5 Wojciech Szczęsny -- 21.7.6 Lionel Messi -- 22 Reproducibility -- 22.1 Package versions for R -- 22.2 Package versions for Python -- Bibliography -- Index Modellierung (DE-588)4170297-9 gnd rswk-swf Data Science (DE-588)1140936166 gnd rswk-swf Prognose (DE-588)4047390-9 gnd rswk-swf Data Science (DE-588)1140936166 s Prognose (DE-588)4047390-9 s Modellierung (DE-588)4170297-9 s DE-604 Burzykowski, Tomasz Sonstige oth Erscheint auch als Druck-Ausgabe Biecek, Przemyslaw Explanatory Model Analysis Milton : CRC Press LLC,c2021 9780367135591 |
| spellingShingle | Biecek, Przemyslaw Explanatory Model Analysis Explore, Explain, and Examine Predictive Models Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Part I Introduction -- 1 Introduction -- 1.1 The aim of the book -- 1.2 A bit of philosophy: three laws of model explanation -- 1.3 Terminology -- 1.4 Black-box models and glass-box models -- 1.5 Model-agnostic and model-specific approach -- 1.6 The structure of the book -- 1.7 What is included in this book and what is not -- 1.8 Acknowledgements -- 2 Model Development -- 2.1 Introduction -- 2.2 Model-development process -- 2.3 Notation -- 2.4 Data understanding -- 2.5 Model assembly (fitting) -- 2.6 Model audit -- 3 Do-it-yourself -- 3.1 Do-it-yourself with R -- 3.1.1 What to install? -- 3.1.2 How to work with DALEX? -- 3.1.3 How to work with archivist? -- 3.2 Do-it-yourself with Python -- 3.2.1 What to install? -- 3.2.2 How to work with dalex? -- 3.2.3 Code snippets for Python -- 4 Datasets and Models -- 4.1 Sinking of the RMS Titanic -- 4.1.1 Data exploration -- 4.2 Models for RMS Titanic, snippets for R -- 4.2.1 Logistic regression model -- 4.2.2 Random forest model -- 4.2.3 Gradient boosting model -- 4.2.4 Support vector machine model -- 4.2.5 Models' predictions -- 4.2.6 Models' explainers -- 4.2.7 List of model-objects -- 4.3 Models for RMS Titanic, snippets for Python -- 4.3.1 Logistic regression model -- 4.3.2 Random forest model -- 4.3.3 Gradient boosting model -- 4.3.4 Support vector machine model -- 4.3.5 Models' predictions -- 4.3.6 Models' explainers -- 4.4 Apartment prices -- 4.4.1 Data exploration -- 4.5 Models for apartment prices, snippets for R -- 4.5.1 Linear regression model -- 4.5.2 Random forest model -- 4.5.3 Support vector machine model -- 4.5.4 Models' predictions -- 4.5.5 Models' explainers -- 4.5.6 List of model-objects -- 4.6 Models for apartment prices, snippets for Python -- 4.6.1 Linear regression model 4.6.2 Random forest model -- 4.6.3 Support vector machine model -- 4.6.4 Models' predictions -- 4.6.5 Models' explainers -- Part II Instance Level -- 5 Introduction to Instance-level Exploration -- 6 Break-down Plots for Additive Attributions -- 6.1 Introduction -- 6.2 Intuition -- 6.3 Method -- 6.3.1 Break-down for linear models -- 6.3.2 Break-down for a general case -- 6.4 Example: Titanic data -- 6.5 Pros and cons -- 6.6 Code snippets for R -- 6.6.1 Basic use of the predict_parts() function -- 6.6.2 Advanced use of the predict_parts() function -- 6.7 Code snippets for Python -- 7 Break-down Plots for Interactions -- 7.1 Intuition -- 7.2 Method -- 7.3 Example: Titanic data -- 7.4 Pros and cons -- 7.5 Code snippets for R -- 7.6 Code snippets for Python -- 8 Shapley Additive Explanations (SHAP) for Average Attributions -- 8.1 Intuition -- 8.2 Method -- 8.3 Example: Titanic data -- 8.4 Pros and cons -- 8.5 Code snippets for R -- 8.6 Code snippets for Python -- 9 Local Interpretable Model-agnostic Explanations (LIME) -- 9.1 Introduction -- 9.2 Intuition -- 9.3 Method -- 9.3.1 Interpretable data representation -- 9.3.2 Sampling around the instance of interest -- 9.3.3 Fitting the glass-box model -- 9.4 Example: Titanic data -- 9.5 Pros and cons -- 9.6 Code snippets for R -- 9.6.1 The lime package -- 9.6.2 The localModel package -- 9.6.3 The iml package -- 9.7 Code snippets for Python -- 10 Ceteris-paribus Profiles -- 10.1 Introduction -- 10.2 Intuition -- 10.3 Method -- 10.4 Example: Titanic data -- 10.5 Pros and cons -- 10.6 Code snippets for R -- 10.6.1 Basic use of the predict_profile() function -- 10.6.2 Advanced use of the predict_profile() function -- 10.6.3 Comparison of models (champion-challenger) -- 10.7 Code snippets for Python -- 11 Ceteris-paribus Oscillations -- 11.1 Introduction -- 11.2 Intuition -- 11.3 Method 11.4 Example: Titanic data -- 11.5 Pros and cons -- 11.6 Code snippets for R -- 11.6.1 Basic use of the predict_parts() function -- 11.6.2 Advanced use of the predict_parts() function -- 11.7 Code snippets for Python -- 12 Local-diagnostics Plots -- 12.1 Introduction -- 12.2 Intuition -- 12.3 Method -- 12.3.1 Nearest neighbors -- 12.3.2 Local-fidelity plot -- 12.3.3 Local-stability plot -- 12.4 Example: Titanic -- 12.5 Pros and cons -- 12.6 Code snippets for R -- 12.7 Code snippets for Python -- 13 Summary of Instance-level Exploration -- 13.1 Introduction -- 13.2 Number of explanatory variables in the model -- 13.2.1 Low to medium number of explanatory variables -- 13.2.2 Medium to a large number of explanatory variables -- 13.2.3 Very large number of explanatory variables -- 13.3 Correlated explanatory variables -- 13.4 Models with interactions -- 13.5 Sparse explanations -- 13.6 Additional uses of model exploration and explanation -- 13.7 Comparison of models (champion-challenger analysis) -- Part III Dataset Level -- 14 Introduction to Dataset-level Exploration -- 15 Model-performance Measures -- 15.1 Introduction -- 15.2 Intuition -- 15.3 Method -- 15.3.1 Continuous dependent variable -- 15.3.1.1 Goodness-of-fit -- 15.3.1.2 Goodness-of-prediction -- 15.3.2 Binary dependent variable -- 15.3.2.1 Goodness-of-fit -- 15.3.2.2 Goodness-of-prediction -- 15.3.3 Categorical dependent variable -- 15.3.3.1 Goodness-of-fit -- 15.3.3.2 Goodness-of-prediction -- 15.3.4 Count dependent variable -- 15.4 Example -- 15.4.1 Apartment prices -- 15.4.2 Titanic data -- 15.5 Pros and cons -- 15.6 Code snippets for R -- 15.7 Code snippets for Python -- 16 Variable-importance Measures -- 16.1 Introduction -- 16.2 Intuition -- 16.3 Method -- 16.4 Example: Titanic data -- 16.5 Pros and cons -- 16.6 Code snippets for R -- 16.7 Code snippets for Python 17 Partial-dependence Profiles -- 17.1 Introduction -- 17.2 Intuition -- 17.3 Method -- 17.3.1 Partial-dependence profiles -- 17.3.2 Clustered partial-dependence profiles -- 17.3.3 Grouped partial-dependence profiles -- 17.3.4 Contrastive partial-dependence profiles -- 17.4 Example: apartment-prices data -- 17.4.1 Partial-dependence profiles -- 17.4.2 Clustered partial-dependence profiles -- 17.4.3 Grouped partial-dependence profiles -- 17.4.4 Contrastive partial-dependence profiles -- 17.5 Pros and cons -- 17.6 Code snippets for R -- 17.6.1 Partial-dependence profiles -- 17.6.2 Clustered partial-dependence profiles -- 17.6.3 Grouped partial-dependence profiles -- 17.6.4 Contrastive partial-dependence profiles -- 17.7 Code snippets for Python -- 17.7.1 Grouped partial-dependence profiles -- 17.7.2 Contrastive partial-dependence profiles -- 18 Local-dependence and Accumulated-local Profiles -- 18.1 Introduction -- 18.2 Intuition -- 18.3 Method -- 18.3.1 Local-dependence profile -- 18.3.2 Accumulated-local profile -- 18.3.3 Dependence profiles for a model with interaction and correlated explanatory variables: an example -- 18.4 Example: apartment-prices data -- 18.5 Pros and cons -- 18.6 Code snippets for R -- 18.7 Code snippets for Python -- 19 Residual-diagnostics Plots -- 19.1 Introduction -- 19.2 Intuition -- 19.3 Method -- 19.4 Example: apartment-prices data -- 19.5 Pros and cons -- 19.6 Code snippets for R -- 19.7 Code snippets for Python -- 20 Summary of Dataset-level Exploration -- 20.1 Introduction -- 20.2 Exploration on training/testing data -- 20.3 Correlated explanatory variables -- 20.4 Comparison of models (champion-challenger analysis) -- Part IV Use-cases -- 21 FIFA 19 -- 21.1 Introduction -- 21.2 Data preparation -- 21.2.1 Code snippets for R -- 21.2.2 Code snippets for Python -- 21.3 Data understanding -- 21.4 Model assembly 21.4.1 Code snippets for R -- 21.4.2 Code snippets for Python -- 21.5 Model audit -- 21.5.1 Code snippets for R -- 21.5.2 Code snippets for Python -- 21.6 Model understanding (dataset-level explanations) -- 21.6.1 Code snippets for R -- 21.6.2 Code snippets for Python -- 21.7 Instance-level explanations -- 21.7.1 Robert Lewandowski -- 21.7.2 Code snippets for R -- 21.7.3 Code snippets for Python -- 21.7.4 CR7 -- 21.7.5 Wojciech Szczęsny -- 21.7.6 Lionel Messi -- 22 Reproducibility -- 22.1 Package versions for R -- 22.2 Package versions for Python -- Bibliography -- Index Modellierung (DE-588)4170297-9 gnd Data Science (DE-588)1140936166 gnd Prognose (DE-588)4047390-9 gnd |
| subject_GND | (DE-588)4170297-9 (DE-588)1140936166 (DE-588)4047390-9 |
| title | Explanatory Model Analysis Explore, Explain, and Examine Predictive Models |
| title_auth | Explanatory Model Analysis Explore, Explain, and Examine Predictive Models |
| title_exact_search | Explanatory Model Analysis Explore, Explain, and Examine Predictive Models |
| title_exact_search_txtP | Explanatory Model Analysis Explore, Explain, and Examine Predictive Models |
| title_full | Explanatory Model Analysis Explore, Explain, and Examine Predictive Models |
| title_fullStr | Explanatory Model Analysis Explore, Explain, and Examine Predictive Models |
| title_full_unstemmed | Explanatory Model Analysis Explore, Explain, and Examine Predictive Models |
| title_short | Explanatory Model Analysis |
| title_sort | explanatory model analysis explore explain and examine predictive models |
| title_sub | Explore, Explain, and Examine Predictive Models |
| topic | Modellierung (DE-588)4170297-9 gnd Data Science (DE-588)1140936166 gnd Prognose (DE-588)4047390-9 gnd |
| topic_facet | Modellierung Data Science Prognose |
| work_keys_str_mv | AT biecekprzemyslaw explanatorymodelanalysisexploreexplainandexaminepredictivemodels AT burzykowskitomasz explanatorymodelanalysisexploreexplainandexaminepredictivemodels |