Explanatory model analysis: explore, explain, and examine predictive models
Gespeichert in:
| Hauptverfasser: | , |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton ; London ; New York
CRC Press, Taylor & Francis Group
2021
|
| Ausgabe: | First edition |
| Schriftenreihe: | Data science series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Literaturverzeichnis: Seite 295-303 |
| Beschreibung: | xiii, 309 Seiten Illustrationen, Diagramme 24 cm |
| ISBN: | 9780367135591 9780367693923 |
Internformat
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| 245 | 1 | 0 | |a Explanatory model analysis |b explore, explain, and examine predictive models |c Przemyslaw Biecek, Tomasz Burzykowski |
| 250 | |a First edition | ||
| 264 | 1 | |a Boca Raton ; London ; New York |b CRC Press, Taylor & Francis Group |c 2021 | |
| 300 | |a xiii, 309 Seiten |b Illustrationen, Diagramme |c 24 cm | ||
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Datensatz im Suchindex
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| adam_text | Contents I Introduction 1 1 Introduction 1.1 The aim of the book.................................................................... 1.2 A bit of philosophy: three laws of modelexplanation.............. 1.3 Terminology .................................................................................... 1.4 Black-box models and glass-box models ................................. 1.5 Model-agnostic and model-specificapproach .......................... 1.6 The structure of the book.............................................................. 1.7 What is included in this book and whatis not ....................... 1.8 Acknowledgements........................................................................ 3 3 6 7 8 10 11 14 14 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 ................................................................................. 17 17 18 20 22 24 28 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 withdalex?................................................ 3.2.3 Code snippets for Python............................................... 29 29 29 30 30 31 31 32 32 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 ............................................... 35 35 36 40 40 40 40 vii
viii Contents 4.2.4 Support vector machinemodel......................................... 4.2.5 Models’ predictions............................................................ 4.2.6 Models’ explainers......................................................... 4.2.7 List of model-objects...................................................... 4.3 Models for RMS Titanic, snippets forPython......................... 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, snippetsfor 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, snippetsfor 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......................................................... 41 41 43 45 46 47 47 47 48 48 49 51 52 54 54 55 55 55 56 57 57 58 58 59 59 59 II Instance Level 61 5 Introduction to Instance-level Exploration 63 6 Break-down Plots for Additive Attributions 65 6.1 Introduction .............................................................................. 65 6.2 Intuition ................................................................................... 65 6.3 Method...................................................................................... 69 6.3.1 Break-down for linear models....................................... 69 6.3.2 Break-down for a general case.......................................... 71 6.4 Example: Titanic data ............................................................ 73 6.5 Pros and cons .......................................................................... 74 6.6 Code snippets forR................................................................... 75 6.6.1 Basic use of the predict_parts() function............... 76 6.6.2 Advanced use of the predict_parts() function............ 77 6.7
Code snippets for Python......................................................... 79 7 Break-down Plots for Interactions 7.1 Intuition .................................................................................... 83 83 Contents 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......................................................... ίχ 85 85 88 89 90 8 Shapley Additive Explanations (SHAP) for Average Attribu tions 93 8.1 8.2 8.3 8.4 8.5 8.6 Intuition .................................................................................... 93 Method....................................................................................... 94 Example: Titanic data ............................................................... 97 Pros and cons ........................................................................... 98 Code snippets for R.................................................................. 99 Code snippets for Python......................................................... 102 9 Local Interpretable Model-agnostic Explanations (LIME) 105 9.1 Introduction .............................................................................. Ю5 9.2 Intuition
.................................................................................... 195 9.3 Method............................................................................................ 107 9.3.1 Interpretable data representation................................. 108 9.3.2 Sampling around the instance of interest..................... 109 9.3.3 Fitting the glass-box model.......................................... 109 9.4 Example: Titanic data ................................................................. Ш 9.5 Pros and cons ................................................................................ Ш 9.6 Code snippets for R.................................................................. ПЗ 9.6.1 The lime package......................................................... 114 9.6.2 The localModel package............................................. 116 9.6.3 The imi package............................................................ 118 9.7 Code snippets for Python......................................................... 120 10 Ceteris-paribus Profiles 123 Introduction .............................................................................. 123 Intuition .................................................................................... 123 Method....................................................................................... 124 Example: Titanic data ............................................................ 124 Pros and cons ........................................................................... 128 Code snippets for
R.................................................................. 129 10.6.1 Basic use of the predict_profile() function............ 130 10.6.2 Advanced use of the predict_prof ile () function . . . 131 10.6.3 Comparison of models (champion-challenger)............ 134 10.7 Code snippets for Python......................................................... 135 10.1 10.2 10.3 10.4 10.5 10.6 11 Ceteris-paribus Oscillations 11.1 Introduction .............................................................................. 11.2 Intuition .................................................................................... 11.3 Method....................................................................................... 139 139 139 140
x Contents 11.4 Example: Titanic data ................................................................. 141 11.5 Pros and cons ........................................................................... 143 11.6 Code snippets for R.................................................................. 143 11.6.1 Basic use of the predict_parts О function............... 144 11.6.2 Advanced use of the predict_parts() function. . . . 145 11.7 Code snippets for Python......................................................... 146 12 Local-diagnostics Plots 147 12.1 Introduction ................................................................................... 147 12.2 Intuition ......................................................................................... 147 12.3 Method...................................................................................... 149 12.3.1 Nearest neighbors ......................................................... 149 12.3.2 Local-fidelity plot............................................................ 150 12.3.3 Local-stability plot.............................................................. 151 12.4 Example: Titanic .......................................................................... 151 12.5 Pros and cons .......................................................................... 152 12.6 Code snippets for R.................................................................. 153 12.7 Code snippets for Python......................................................... 156 13 Summary of Instance-level Exploration 157 13.1 Introduction
................................................................................... 157 13.2 Number of explanatory variables in the model .................... 158 13.2.1 Low to medium number of explanatory variables . . . 159 13.2.2 Medium to a large number of explanatory variables . . 159 13.2.3 Very large number of explanatory variables............... 159 13.3 Correlated explanatory variables............................................. 160 13.4 Models with interactions ......................................................... 160 13.5 Sparse explanations ....................................................................... 161 13.6 Additional uses of model exploration and explanation ...............161 13.7 Comparison of models (champion-challenger analysis) .... 162 III Dataset Level 14 Introduction to Dataset-level Exploration 167 169 15 Model-performance Measures 171 15.1 Introduction ................................................................................... 171 15.2 Intuition .................................................................................... 172 15.3 Method...................................................................................... 173 15.3.1 Continuous dependent variable.................................... 173 15.3.1.1 Goodness-of-fit................................................ 173 15.3.1.2 Goodness-of-prediction................................. 174 15.3.2 Binary dependent variable............................................. 176 15.3.2.1 Goodness-of-fit................................................ 176 15.3.2.2 Goodness-
of-prediction...................................... 177 15.3.3 Categorical dependent variable..........................................181 Contents 15.4 15.5 15.6 15.7 15.3.3.1 Goodness-of-fit......................................................181 15.3.3.2 Goodness-of-prediction.......................................181 15.3.4 Count dependent variable............................................ 182 Example ...................................................................................... 183 15.4.1 Apartment prices............................................................ 183 15.4.2 Titanic data .................................................................. 183 Pros and cons ............................................................................. 186 Code snippets for R....................................................................... 187 Code snippets for Python........................................................... 189 16 Variable-importance Measures 16.1 16.2 16.3 16.4 16.5 16.6 16.7 χί Introduction .......................................................................... Intuition ................................................................................... Method...................................................................................... Example: Titanicdata ............................................................. Pros and cons .......................................................................... Code snippets forR................................................................. Code snippets
forPython......................................................... 17 Partial-dependence Profiles 193 193 194 194 195 198 198 204 207 17.1 Introduction ................................................................................207 17.2 Intuition ................................................................................... 208 17.3 Method...................................................................................... 208 17.3.1 Partial-dependence profiles.......................................... 208 17.3.2 Clustered partial-dependence profiles........................... 209 17.3.3 Grouped partial-dependence profiles........................... 210 17.3.4 Contrastive partial-dependence profiles..............................211 17.4 Example: apartment-prices data ................................................ 212 17.4.1 Partial-dependence profiles.......................................... 212 17.4.2 Clustered partial-dependence profiles........................... 213 17.4.3 Grouped partial-dependence profiles........................... 214 17.4.4 Contrastive partial-dependence profiles........................ 215 17.5 Pros and cons ............................................................................. 216 17.6 Code snippets for R....................................................................... 217 17.6.1 Partial-dependence profiles............................................... 217 17.6.2 Clustered partial-dependence profiles........................... 219 17.6.3 Grouped partial-dependence profiles........................... 219 17.6.4
Contrastive partial-dependence profiles........................ 219 17.7 Code snippets for Python......................................................... 220 17.7.1 Grouped partial-dependence profiles........................... 222 17.7.2 Contrastive partial-dependence profiles........................ 224 18 Local-dependence and Accumulated-local Profiles 227 18.1 Introduction ................................................................................ 227 18.2 Intuition ......................................................................................... 227
Contents xii 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........................................................... Use-cases ХШ 232 232 234 21.7.4 CR7.................................................................................... 285 21.7.5 Wojciech
Szczęsny.................................................................287 21.7.6 Lionel Messi....................................................................... 289 235 238 239 240 242 22 Reproducibility 291 22.1 Package versions for R ....................................................................291 22.2 Package versions for Python ..................................................... 293 Bibliography 295 245 245 246 246 250 255 255 258 Index 305 20 Summary of Dataset-level Exploration 261 20.1 Introduction .......................................................................................261 20.2 Exploration on training/testing data......................................... 262 20.3 Correlated explanatory variables............................................... 263 20.4 Comparison of models (champion-challenger analysis) .... 264 IV Contents 265 21 FIFA 19 267 21.1 Introduction ...................................................................................... 267 21.2 Data preparation ............................................................................. 267 21.2.1 Code snippets for R........................................................ 269 21.2.2 Code snippets for Python............................................... 269 21.3 Data understanding .................................................................... 270 21.4 Model assembly ................................................................................ 271 21.4.1 Code snippets for R........................................................ 272 21.4.2 Code snippets for
Python............................................... 273 21.5 Model audit ................................................................................. 273 21.5.1 Code snippets for R........................................................ 274 21.5.2 Code snippets for Python............................................... 275 21.6 Model understanding (dataset-level explanations) ................ 276 21.6.1 Code snippets for R........................................................ 279 21.6.2 Code snippets for Python............................................... 279 21.7 Instance-level explanations ........................................................ 280 21.7.1 Robert Lewandowski........................................................ 280 21.7.2 Code snippets for R........................................................ 284 21.7.3 Code snippets for Python............................................... 285
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| adam_txt |
Contents I Introduction 1 1 Introduction 1.1 The aim of the book. 1.2 A bit of philosophy: three laws of modelexplanation. 1.3 Terminology . 1.4 Black-box models and glass-box models . 1.5 Model-agnostic and model-specificapproach . 1.6 The structure of the book. 1.7 What is included in this book and whatis not . 1.8 Acknowledgements. 3 3 6 7 8 10 11 14 14 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 . 17 17 18 20 22 24 28 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 withdalex?. 3.2.3 Code snippets for Python. 29 29 29 30 30 31 31 32 32 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 . 35 35 36 40 40 40 40 vii
viii Contents 4.2.4 Support vector machinemodel. 4.2.5 Models’ predictions. 4.2.6 Models’ explainers. 4.2.7 List of model-objects. 4.3 Models for RMS Titanic, snippets forPython. 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, snippetsfor 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, snippetsfor 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. 41 41 43 45 46 47 47 47 48 48 49 51 52 54 54 55 55 55 56 57 57 58 58 59 59 59 II Instance Level 61 5 Introduction to Instance-level Exploration 63 6 Break-down Plots for Additive Attributions 65 6.1 Introduction . 65 6.2 Intuition . 65 6.3 Method. 69 6.3.1 Break-down for linear models. 69 6.3.2 Break-down for a general case. 71 6.4 Example: Titanic data . 73 6.5 Pros and cons . 74 6.6 Code snippets forR. 75 6.6.1 Basic use of the predict_parts() function. 76 6.6.2 Advanced use of the predict_parts() function. 77 6.7
Code snippets for Python. 79 7 Break-down Plots for Interactions 7.1 Intuition . 83 83 Contents 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. ίχ 85 85 88 89 90 8 Shapley Additive Explanations (SHAP) for Average Attribu tions 93 8.1 8.2 8.3 8.4 8.5 8.6 Intuition . 93 Method. 94 Example: Titanic data . 97 Pros and cons . 98 Code snippets for R. 99 Code snippets for Python. 102 9 Local Interpretable Model-agnostic Explanations (LIME) 105 9.1 Introduction . Ю5 9.2 Intuition
. 195 9.3 Method. 107 9.3.1 Interpretable data representation. 108 9.3.2 Sampling around the instance of interest. 109 9.3.3 Fitting the glass-box model. 109 9.4 Example: Titanic data . Ш 9.5 Pros and cons . Ш 9.6 Code snippets for R. ПЗ 9.6.1 The lime package. 114 9.6.2 The localModel package. 116 9.6.3 The imi package. 118 9.7 Code snippets for Python. 120 10 Ceteris-paribus Profiles 123 Introduction . 123 Intuition . 123 Method. 124 Example: Titanic data . 124 Pros and cons . 128 Code snippets for
R. 129 10.6.1 Basic use of the predict_profile() function. 130 10.6.2 Advanced use of the predict_prof ile () function . . . 131 10.6.3 Comparison of models (champion-challenger). 134 10.7 Code snippets for Python. 135 10.1 10.2 10.3 10.4 10.5 10.6 11 Ceteris-paribus Oscillations 11.1 Introduction . 11.2 Intuition . 11.3 Method. 139 139 139 140
x Contents 11.4 Example: Titanic data . 141 11.5 Pros and cons . 143 11.6 Code snippets for R. 143 11.6.1 Basic use of the predict_parts О function. 144 11.6.2 Advanced use of the predict_parts() function. . . . 145 11.7 Code snippets for Python. 146 12 Local-diagnostics Plots 147 12.1 Introduction . 147 12.2 Intuition . 147 12.3 Method. 149 12.3.1 Nearest neighbors . 149 12.3.2 Local-fidelity plot. 150 12.3.3 Local-stability plot. 151 12.4 Example: Titanic . 151 12.5 Pros and cons . 152 12.6 Code snippets for R. 153 12.7 Code snippets for Python. 156 13 Summary of Instance-level Exploration 157 13.1 Introduction
. 157 13.2 Number of explanatory variables in the model . 158 13.2.1 Low to medium number of explanatory variables . . . 159 13.2.2 Medium to a large number of explanatory variables . . 159 13.2.3 Very large number of explanatory variables. 159 13.3 Correlated explanatory variables. 160 13.4 Models with interactions . 160 13.5 Sparse explanations . 161 13.6 Additional uses of model exploration and explanation .161 13.7 Comparison of models (champion-challenger analysis) . 162 III Dataset Level 14 Introduction to Dataset-level Exploration 167 169 15 Model-performance Measures 171 15.1 Introduction . 171 15.2 Intuition . 172 15.3 Method. 173 15.3.1 Continuous dependent variable. 173 15.3.1.1 Goodness-of-fit. 173 15.3.1.2 Goodness-of-prediction. 174 15.3.2 Binary dependent variable. 176 15.3.2.1 Goodness-of-fit. 176 15.3.2.2 Goodness-
of-prediction. 177 15.3.3 Categorical dependent variable.181 Contents 15.4 15.5 15.6 15.7 15.3.3.1 Goodness-of-fit.181 15.3.3.2 Goodness-of-prediction.181 15.3.4 Count dependent variable. 182 Example . 183 15.4.1 Apartment prices. 183 15.4.2 Titanic data . 183 Pros and cons . 186 Code snippets for R. 187 Code snippets for Python. 189 16 Variable-importance Measures 16.1 16.2 16.3 16.4 16.5 16.6 16.7 χί Introduction . Intuition . Method. Example: Titanicdata . Pros and cons . Code snippets forR. Code snippets
forPython. 17 Partial-dependence Profiles 193 193 194 194 195 198 198 204 207 17.1 Introduction .207 17.2 Intuition . 208 17.3 Method. 208 17.3.1 Partial-dependence profiles. 208 17.3.2 Clustered partial-dependence profiles. 209 17.3.3 Grouped partial-dependence profiles. 210 17.3.4 Contrastive partial-dependence profiles.211 17.4 Example: apartment-prices data . 212 17.4.1 Partial-dependence profiles. 212 17.4.2 Clustered partial-dependence profiles. 213 17.4.3 Grouped partial-dependence profiles. 214 17.4.4 Contrastive partial-dependence profiles. 215 17.5 Pros and cons . 216 17.6 Code snippets for R. 217 17.6.1 Partial-dependence profiles. 217 17.6.2 Clustered partial-dependence profiles. 219 17.6.3 Grouped partial-dependence profiles. 219 17.6.4
Contrastive partial-dependence profiles. 219 17.7 Code snippets for Python. 220 17.7.1 Grouped partial-dependence profiles. 222 17.7.2 Contrastive partial-dependence profiles. 224 18 Local-dependence and Accumulated-local Profiles 227 18.1 Introduction . 227 18.2 Intuition . 227
Contents xii 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. Use-cases ХШ 232 232 234 21.7.4 CR7. 285 21.7.5 Wojciech
Szczęsny.287 21.7.6 Lionel Messi. 289 235 238 239 240 242 22 Reproducibility 291 22.1 Package versions for R .291 22.2 Package versions for Python . 293 Bibliography 295 245 245 246 246 250 255 255 258 Index 305 20 Summary of Dataset-level Exploration 261 20.1 Introduction .261 20.2 Exploration on training/testing data. 262 20.3 Correlated explanatory variables. 263 20.4 Comparison of models (champion-challenger analysis) . 264 IV Contents 265 21 FIFA 19 267 21.1 Introduction . 267 21.2 Data preparation . 267 21.2.1 Code snippets for R. 269 21.2.2 Code snippets for Python. 269 21.3 Data understanding . 270 21.4 Model assembly . 271 21.4.1 Code snippets for R. 272 21.4.2 Code snippets for
Python. 273 21.5 Model audit . 273 21.5.1 Code snippets for R. 274 21.5.2 Code snippets for Python. 275 21.6 Model understanding (dataset-level explanations) . 276 21.6.1 Code snippets for R. 279 21.6.2 Code snippets for Python. 279 21.7 Instance-level explanations . 280 21.7.1 Robert Lewandowski. 280 21.7.2 Code snippets for R. 284 21.7.3 Code snippets for Python. 285 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Biecek, Przemysław Burzykowski, Tomasz |
| author_GND | (DE-588)1236147464 (DE-588)1213542766 |
| author_facet | Biecek, Przemysław Burzykowski, Tomasz |
| author_role | aut aut |
| author_sort | Biecek, Przemysław |
| author_variant | p b pb t b tb |
| building | Verbundindex |
| bvnumber | BV047328081 |
| classification_rvk | SK 950 QH 230 QH 234 |
| ctrlnum | (OCoLC)1252757504 (DE-599)KXP1746711779 |
| discipline | Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Mathematik Wirtschaftswissenschaften |
| edition | First edition |
| format | Book |
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| id | DE-604.BV047328081 |
| illustrated | Illustrated |
| index_date | 2024-07-03T17:31:44Z |
| indexdate | 2024-07-10T09:09:01Z |
| institution | BVB |
| isbn | 9780367135591 9780367693923 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032730673 |
| oclc_num | 1252757504 |
| open_access_boolean | |
| owner | DE-739 DE-11 DE-355 DE-BY-UBR DE-384 |
| owner_facet | DE-739 DE-11 DE-355 DE-BY-UBR DE-384 |
| physical | xiii, 309 Seiten Illustrationen, Diagramme 24 cm |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | CRC Press, Taylor & Francis Group |
| record_format | marc |
| series2 | Data science series |
| spelling | Biecek, Przemysław Verfasser (DE-588)1236147464 aut Explanatory model analysis explore, explain, and examine predictive models Przemyslaw Biecek, Tomasz Burzykowski First edition Boca Raton ; London ; New York CRC Press, Taylor & Francis Group 2021 xiii, 309 Seiten Illustrationen, Diagramme 24 cm txt rdacontent n rdamedia nc rdacarrier Data science series Literaturverzeichnis: Seite 295-303 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 Verfasser (DE-588)1213542766 aut Erscheint auch als Online-Ausgabe 978-0-429-02719-2 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032730673&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Biecek, Przemysław Burzykowski, Tomasz Explanatory model analysis explore, explain, and examine predictive models 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 Przemyslaw Biecek, Tomasz Burzykowski |
| title_fullStr | Explanatory model analysis explore, explain, and examine predictive models Przemyslaw Biecek, Tomasz Burzykowski |
| title_full_unstemmed | Explanatory model analysis explore, explain, and examine predictive models Przemyslaw Biecek, Tomasz Burzykowski |
| 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 |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032730673&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT biecekprzemysław explanatorymodelanalysisexploreexplainandexaminepredictivemodels AT burzykowskitomasz explanatorymodelanalysisexploreexplainandexaminepredictivemodels |