Feature engineering and selection: a practical approach for predictive models
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton ; London ; New York
CRC Press, Taylor & Francis Group
[2020]
|
| Schriftenreihe: | Data science series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | xv, 297 Seiten Diagramme 27 cm |
| ISBN: | 9781138079229 9781032090856 |
Internformat
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Datensatz im Suchindex
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| adam_text |
Contents Preface · xi Author Bios · xv 1 Introduction · 1 1.1 1.2 1.3 1.4 1.5 1.6 A Simple Example · 4 Important Concepts · 7 A More Complex Example · 15 Feature Selection · 17 An Outline of the Book · 18 Computing · 20 2 Illustrative Example: Predicting Risk of Ischemic Stroke · 21 2.1 2.2 2.3 2.4 2.5 2.6 Splitting · 23 Preprocessing · 23 Exploration · 26 Predictive Modeling across Sets · 30 Other Considerations · 34 Computing · 34 3 A Review of the Predictive Modeling Process · 35 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 Illustrative Example: OkCupid Profile Data · 35 Measuring Performance · 36 Data Splitting · 46 Resampling · 47 Tuning Parameters and Overfitting · 56 Model Optimization and Tuning · 57 Comparing Models Using the Training Set · 61 Feature Engineering without Overfitting · 62 Summary · 64 Computing · 64 4 Exploratory Visualizations · 65 4.1 Introduction to the Chicago Train Ridership Data · 66 vii
Contents 4.2 4.3 4.4 4.5 4.6 Visualizations for Numeric Data: Exploring Train Ridership Data · 69 Visualizations for Categorical Data: Exploring the OkCupid Data · 83 Postmodeling Exploratory Visualizations · 88 Summary · 92 Computing · 92 5 Encoding Categorical Predictors · 93 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 Creating Dummy Variables for Unordered Categories · 94 Encoding Predictors with Many Categories · 96 Approaches for Novel Categories · 102 Supervised Encoding Methods · 102 Encodings for Ordered Data · 107 Creating Features from Text Data · 109 Factors versus Dummy Variables in Tree-Based Models · 114 Summary · 119 Computing · 120 6 Engineering Numeric Predictors · 121 6.1 6.2 6.3 6.4 6.5 1:1 Transformations · 122 l:Many Transformations · 126 Many.-Many Transformations · 133 Summary · 154 Computing · 155 7 Detecting Interaction Effects · 157 7.1 7.2 7.3 7.4 7.5 7.6 7.7 Guiding Principles in the Search for Interactions · 161 Practical Considerations · 164 The Brute-Force Approach to Identifying Predictive Interactions · 165 Approaches when Complete Enumeration Is Practically Impossible · 172 Other Potentially Useful Tools · 184 Summary · 185 Computing · 186 8 Handling Missing Data · 187 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 Understanding the Nature and Severity of Missing Information · 189 Models that Are Resistant to Missing Values · 195 Deletion of Data · 196 Encoding Missingness · 197 Imputation Methods · 198 Special Cases · 203 Summary · 203 Computing · 204 9 Working with Profile Data · 205 9.1 9.2 9.3 9.4 9.5 Illustrative Data: Pharmaceutical Manufacturing Monitoring
· 209 What Are the Experimental Unit and the Unit of Prediction? · 210 Reducing Background · 214 Reducing Other Noise · 215 Exploiting Correlation · 217
Contents їх 9.6 Impacts of Data Processing on Modeling · 219 9.7 Summary · 224 9.8 Computing · 225 10 Feature Selection Overview · 227 10.1 10.2 10.3 10.4 10.5 10.6 10.7 Goals of Feature Selection · 227 Classes of Feature Selection Methodologies · 228 Effect of Irrelevant Features · 232 Overfitting to Predictors and External Validation · 235 A Case Study · 238 Next Steps · 240 Computing · 240 11 Greedy Search Methods · 241 11.1 11.2 11.3 11.4 11.5 11.6 Illustrative Data: Predicting Parkinson’s Disease Simple Filters · 241 Recursive Feature Elimination · 248 Stepwise Selection · 252 Summary · 254 Computing · 255 12 Global Search Methods · 257 12.1 12.2 12.3 12.4 12.5 12.6 Naive Bayes Models · 257 Simulated Annealing · 260 Genetic Algorithms · 270 Test Set Results · 280 Summary · 281 Computing · 282 Bibliography · 283 Index · 295 · 241 |
| adam_txt |
Contents Preface · xi Author Bios · xv 1 Introduction · 1 1.1 1.2 1.3 1.4 1.5 1.6 A Simple Example · 4 Important Concepts · 7 A More Complex Example · 15 Feature Selection · 17 An Outline of the Book · 18 Computing · 20 2 Illustrative Example: Predicting Risk of Ischemic Stroke · 21 2.1 2.2 2.3 2.4 2.5 2.6 Splitting · 23 Preprocessing · 23 Exploration · 26 Predictive Modeling across Sets · 30 Other Considerations · 34 Computing · 34 3 A Review of the Predictive Modeling Process · 35 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 Illustrative Example: OkCupid Profile Data · 35 Measuring Performance · 36 Data Splitting · 46 Resampling · 47 Tuning Parameters and Overfitting · 56 Model Optimization and Tuning · 57 Comparing Models Using the Training Set · 61 Feature Engineering without Overfitting · 62 Summary · 64 Computing · 64 4 Exploratory Visualizations · 65 4.1 Introduction to the Chicago Train Ridership Data · 66 vii
Contents 4.2 4.3 4.4 4.5 4.6 Visualizations for Numeric Data: Exploring Train Ridership Data · 69 Visualizations for Categorical Data: Exploring the OkCupid Data · 83 Postmodeling Exploratory Visualizations · 88 Summary · 92 Computing · 92 5 Encoding Categorical Predictors · 93 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 Creating Dummy Variables for Unordered Categories · 94 Encoding Predictors with Many Categories · 96 Approaches for Novel Categories · 102 Supervised Encoding Methods · 102 Encodings for Ordered Data · 107 Creating Features from Text Data · 109 Factors versus Dummy Variables in Tree-Based Models · 114 Summary · 119 Computing · 120 6 Engineering Numeric Predictors · 121 6.1 6.2 6.3 6.4 6.5 1:1 Transformations · 122 l:Many Transformations · 126 Many.-Many Transformations · 133 Summary · 154 Computing · 155 7 Detecting Interaction Effects · 157 7.1 7.2 7.3 7.4 7.5 7.6 7.7 Guiding Principles in the Search for Interactions · 161 Practical Considerations · 164 The Brute-Force Approach to Identifying Predictive Interactions · 165 Approaches when Complete Enumeration Is Practically Impossible · 172 Other Potentially Useful Tools · 184 Summary · 185 Computing · 186 8 Handling Missing Data · 187 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 Understanding the Nature and Severity of Missing Information · 189 Models that Are Resistant to Missing Values · 195 Deletion of Data · 196 Encoding Missingness · 197 Imputation Methods · 198 Special Cases · 203 Summary · 203 Computing · 204 9 Working with Profile Data · 205 9.1 9.2 9.3 9.4 9.5 Illustrative Data: Pharmaceutical Manufacturing Monitoring
· 209 What Are the Experimental Unit and the Unit of Prediction? · 210 Reducing Background · 214 Reducing Other Noise · 215 Exploiting Correlation · 217
Contents їх 9.6 Impacts of Data Processing on Modeling · 219 9.7 Summary · 224 9.8 Computing · 225 10 Feature Selection Overview · 227 10.1 10.2 10.3 10.4 10.5 10.6 10.7 Goals of Feature Selection · 227 Classes of Feature Selection Methodologies · 228 Effect of Irrelevant Features · 232 Overfitting to Predictors and External Validation · 235 A Case Study · 238 Next Steps · 240 Computing · 240 11 Greedy Search Methods · 241 11.1 11.2 11.3 11.4 11.5 11.6 Illustrative Data: Predicting Parkinson’s Disease Simple Filters · 241 Recursive Feature Elimination · 248 Stepwise Selection · 252 Summary · 254 Computing · 255 12 Global Search Methods · 257 12.1 12.2 12.3 12.4 12.5 12.6 Naive Bayes Models · 257 Simulated Annealing · 260 Genetic Algorithms · 270 Test Set Results · 280 Summary · 281 Computing · 282 Bibliography · 283 Index · 295 · 241 |
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| spelling | Kuhn, Max Verfasser (DE-588)1093876328 aut Feature engineering and selection a practical approach for predictive models Max Kuhn, Kjell Johnson Boca Raton ; London ; New York CRC Press, Taylor & Francis Group [2020] xv, 297 Seiten Diagramme 27 cm txt rdacontent n rdamedia nc rdacarrier Data science series Includes bibliographical references and index Merkmalsextraktion (DE-588)4314440-8 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Mustererkennung (DE-588)4040936-3 gnd rswk-swf Predictive control / Data processing Predictive control / Mathematical models R (Computer program language) Predictive control ; Mathematical models R Programm (DE-588)4705956-4 s Mustererkennung (DE-588)4040936-3 s Merkmalsextraktion (DE-588)4314440-8 s DE-604 Johnson, Kjell Verfasser (DE-588)109387998X aut Erscheint auch als Online-Ausgabe 9781351609470 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032805223&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Kuhn, Max Johnson, Kjell Feature engineering and selection a practical approach for predictive models Merkmalsextraktion (DE-588)4314440-8 gnd R Programm (DE-588)4705956-4 gnd Mustererkennung (DE-588)4040936-3 gnd |
| subject_GND | (DE-588)4314440-8 (DE-588)4705956-4 (DE-588)4040936-3 |
| title | Feature engineering and selection a practical approach for predictive models |
| title_auth | Feature engineering and selection a practical approach for predictive models |
| title_exact_search | Feature engineering and selection a practical approach for predictive models |
| title_exact_search_txtP | Feature engineering and selection a practical approach for predictive models |
| title_full | Feature engineering and selection a practical approach for predictive models Max Kuhn, Kjell Johnson |
| title_fullStr | Feature engineering and selection a practical approach for predictive models Max Kuhn, Kjell Johnson |
| title_full_unstemmed | Feature engineering and selection a practical approach for predictive models Max Kuhn, Kjell Johnson |
| title_short | Feature engineering and selection |
| title_sort | feature engineering and selection a practical approach for predictive models |
| title_sub | a practical approach for predictive models |
| topic | Merkmalsextraktion (DE-588)4314440-8 gnd R Programm (DE-588)4705956-4 gnd Mustererkennung (DE-588)4040936-3 gnd |
| topic_facet | Merkmalsextraktion R Programm Mustererkennung |
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