Automated machine learning for business:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York
Oxford University Press
[2021]
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xvii, 328 Seiten Illustrationen, Diagramme |
| ISBN: | 9780190941659 9780190941666 |
Internformat
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| adam_text | Contents Preface Automated Machine Learning (AutoML) A Notetolnstructors Acknowledgments Book Outline Dataset Download Copyrights SECTION I: WHY USE AUTOMATED MACHINE LEARNING? 1. What Is Machine Learning? 1.1 WhyLearnThis? 1.2 MachineLearninglsEverywhere 1.3 What Is Machine Learning? 1.4 DataforMachine Learning 1.5 Exercises 3 3 4 6 8 JO 2. Automating Machine Learning 2.1 What Is Automated Machine Learning? 2.2 What Automated MachineLearningls Not 2.3 AvailableToolsandPlatforms 2.4 Eight Criteria for AutoML Excellence 2.5 How Do the Fundamental Principles of Machine Learning and Artificial Intelligence Transfer to AutoML? A Point-by-Point Evaluation. 2.6 Exercises 11 12 14 15 17 20 21 SECTION II: DEFINING PROJECT OBJECTIVES 3. Specifyßus/nessProblem 3.1 Why Start with a Business Problem? 3.2 Problem Statements 3.3 Exercises 25 25 26 29 4. Acquire Subject Matter Expertise 4.1 Importance of Subject Matter Expertise 4.2 Exercises 31 31 32 5. Define Prediction Target 5.1 WhatlsaPrediction Target? 5.2 How Is the Target Important for Machine Learning? 5.3 Exercises/Discussion 33 33 35 Յ6
Contents 6. DecideonUmtofAnalysis 6.1 WhatlsaUnitofAnalysis? 6.2 How to Determine Unit of Analysis 6.3 Exercises 7. Success, Risk, and Continuation 7.1 7.2 7.3 7.4 Identify Success Criteria Foresee Risks DecideWhethertoContinue Exercises 37 37 38 39 40 40 41 44 45 SECTION III: ACQUIRE AND INTEGRATE DATA 8. AccessingandStoringData 8.1 8.2 8.3 8.4 Track Down Relevant Data ExamineDataandRemoveColumns Example Dataset Exercises 9. Data Integration 51 51 54 55 58 59 9.1 Joins 9.2 Exercises 60 69 10. Data Transformations 70 10.1 10.2 10.3 Splitting and Extracting New Columns 10.1.1 IF-THENStatementsand One-hot Encoding 10.1.2 RegularExpressions(RegEx) Transformations Exercises 11. Summarization 11.1 11.2 11.3 Summarize Crosstab Exercises 12. Data Reduction and Splitting 70 70 72 78 79 80 80 84 87 88 12.1 12.2 Unique Rows Filtering 88 91 12.3 12.4 Combining the data Exercises 92 94 SECTION IV: MODEL DATA 13. Startup Processes 13.1 13.2 Uploading Data Exercise 14. Feature Understanding and Setection 14.1 14.2 Descriptive Statistics Data Types 97 97 102 103 103 107
Contents vìi 14.3 14.4 Evaluations of Feature Content MissingValues 14.5 Exercises 113 15. Build Candidate Models 114 15.1 StartingtheProcess 15.2 Advanced Options 15.3 StartingtheAnalyticalProcess 15.4 Model Selection Process 15.4.1 Tournament Round 1:32% Sample 15.4.2 Tournament Round 2:64% Sample 15.4.3 TournamentRound3:CrossValidation 15.4.4 Tournament Round 4: Blending 15.5 Exercises 16. Understandingthe Process 16.1 16.2 16.3 16.4 16.5 114 116 121 127 128 131 131 132 133 134 LearningCurvesand Speed Accuraсу Tradeoffs Blueprints 16.3.1 NumericDataCleansmg(lmputation) 16.3.2 Standardization 16.3.3 One-hot Encoding 16.3.4 Ordinal Encoding 134 138 139 140 142 143 147 16.3.5 MatrixofWord-gramOccurrences 16.3.6 Classification Hyperparameter Optimization (Advanced Content) Exercises 149 151 154 156 17. Evaluate Model Performance 17.1 Introduction 17.2 ASampleAlgorithmandModel 17.3 ROC Curve 17.4 UsingtheLiftChartandProfitCurveforBusmessDecisions 17.5 Exercises 18. Comparing Model Pairs 18.1 18.2 110 112 Model Comparison PrioritizingModelingCriteriaandSelectingaModel 18.3 Exercises 157 157 159 164 176 179 180 180 185 187 SECTION V: INTERPRET AND COMMUNICATE 19. Interpret Model 19.1 Feature Impactson Target 19.2 The Overall impact of Features on the Target without ConsiderationofOtherFeatures 19.3 The Overall Impact of a Feature Adjusted for the Impact of Other Features 19.4 TheDirectionallmpactofFeaturesonTarget 19.5 ThePartiallmpactofFeaturesonTarget 191 191 192 193 194 195
viii Contents 19.6 ThePowerofLanguage 19.7 19.8 Hotspots Prediction Explanations 19.9 Exercises 20. Communicate Model Insights 198 201 203 205 206 20.1 Unlocking Holdout 20.2 Business Problem First 20.3 Pre-processing and Model Quality Metrics 20.4 Areas Where the Model Struggles 20.5 Most Predictive Features 20.6 NotAIIFeaturesAreCreatedEqual 207 209 210 213 214 214 20.7 20.8 217 218 Recommended BusinessActions Exercises SECTION VI: IMPLEMENT, DOCUMENT AND MAINTAIN 21. Set Up Prediction System 21.1 Retraining Model 21.2 21.3 Choose Deployment Strategy Exercises 22. Document Modeling Process for Reproducibility 22.1 22.2 Model Documentation Exercises 23. Create Model Monitoringand Maintenance Plan 23.1 23.2 23.3 Potential Problems Strategies Exercises 24. Seven Types of Target Leakage in Machine Learning and an Exercise 24.1 TypesofTargetLeakage 24.2 A Hands-on Exercise in DetectingTarget Leakage 24.3 Exercises 25. Time-Aware Modeling 25.1 An Example of Time-Aware Modeling 25.1.1 Problem Statement 25.1.2 Data 25.1.3 Initialize Analysis 25.1.4 Time-Aware Modeling Background 25.1.5 Data Preparation 25.1.6 Model Buildingand Residuals 25.1.7 Candidate Models 25.1.8 Selectingand Examininga Model 25.1.9 A Small Detour into Residuals 25.1.10 Model Value 25.1.11 LearningaboutAvocadoPriceDrivers 25.2 Exercises 221 221 222 227 228 228 229 230 230 230 232 233 233 236 239 240 240 240 241 241 241 244 247 247 249 253 256 256 258
Contents 26. Time-Series Modeling 26.1 The Assumptions of Time-Series Machine Learning 26.2 A Hands-on Exercise in Time-Series Analysis 26.2.1 Problem Context 26.2.2 Loading Data 26.2.3 SpecifyTimeUnitandGenerateFeatures 26.2.3 Examine Candidate Models 26.2.4 Digging into the Preferred Model 26.2.5 Predicting 26.3 Exercises 259 259 260 260 262 262 268 270 273 275 Appendix A. Datasets A.l Diabetes Patients Réadmissions Summary BusinessGoal Datasets Exercises Rights A.2 Luxury Shoes Summary 277 277 277 277 277 280 280 280 280 BusinessGoal Datasets Exercises A.3 Boston Airbnb Summary BusinessGoal Datasets Rights A.4 Part Backorders Summary BusinessGoal Datasets Exercises Rights A.5 Student Grades Portuguese Summary 281 281 283 283 283 284 284 287 287 287 287 287 288 289 289 289 BusinessGoal Datasets Exercises 289 289 290 Rights A.6 LendingClub Summary 293 293 293 BusinessGoal Dataset Rights A.7 CollegeStartingSalaries Summary 294 294 300 300 300 BusinessGoal Datasets Exercises 300 300 301 Rights 301
x Contents A.8 HRAttrition Summary Business Goal Datasets Exercises Rights A.9 Avocadopocalypse Now? Summary Business Goal Datasets Exercises Rights AppendixB. Optimization and Sorting Measures Appendixe. More on Cross Validation ЗОЇ ЗОЇ ՅՕշ ՅՕշ 304 305 305 305 3О6 3О6 307 307 ՅՕ8 311 References Index 315 319
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| adam_txt |
Contents Preface Automated Machine Learning (AutoML) A Notetolnstructors Acknowledgments Book Outline Dataset Download Copyrights SECTION I: WHY USE AUTOMATED MACHINE LEARNING? 1. What Is Machine Learning? 1.1 WhyLearnThis? 1.2 MachineLearninglsEverywhere 1.3 What Is Machine Learning? 1.4 DataforMachine Learning 1.5 Exercises 3 3 4 6 8 JO 2. Automating Machine Learning 2.1 What Is Automated Machine Learning? 2.2 What Automated MachineLearningls Not 2.3 AvailableToolsandPlatforms 2.4 Eight Criteria for AutoML Excellence 2.5 How Do the Fundamental Principles of Machine Learning and Artificial Intelligence Transfer to AutoML? A Point-by-Point Evaluation. 2.6 Exercises 11 12 14 15 17 20 21 SECTION II: DEFINING PROJECT OBJECTIVES 3. Specifyßus/nessProblem 3.1 Why Start with a Business Problem? 3.2 Problem Statements 3.3 Exercises 25 25 26 29 4. Acquire Subject Matter Expertise 4.1 Importance of Subject Matter Expertise 4.2 Exercises 31 31 32 5. Define Prediction Target 5.1 WhatlsaPrediction Target? 5.2 How Is the Target Important for Machine Learning? 5.3 Exercises/Discussion 33 33 35 Յ6
Contents 6. DecideonUmtofAnalysis 6.1 WhatlsaUnitofAnalysis? 6.2 How to Determine Unit of Analysis 6.3 Exercises 7. Success, Risk, and Continuation 7.1 7.2 7.3 7.4 Identify Success Criteria Foresee Risks DecideWhethertoContinue Exercises 37 37 38 39 40 40 41 44 45 SECTION III: ACQUIRE AND INTEGRATE DATA 8. AccessingandStoringData 8.1 8.2 8.3 8.4 Track Down Relevant Data ExamineDataandRemoveColumns Example Dataset Exercises 9. Data Integration 51 51 54 55 58 59 9.1 Joins 9.2 Exercises 60 69 10. Data Transformations 70 10.1 10.2 10.3 Splitting and Extracting New Columns 10.1.1 IF-THENStatementsand One-hot Encoding 10.1.2 RegularExpressions(RegEx) Transformations Exercises 11. Summarization 11.1 11.2 11.3 Summarize Crosstab Exercises 12. Data Reduction and Splitting 70 70 72 78 79 80 80 84 87 88 12.1 12.2 Unique Rows Filtering 88 91 12.3 12.4 Combining the data Exercises 92 94 SECTION IV: MODEL DATA 13. Startup Processes 13.1 13.2 Uploading Data Exercise 14. Feature Understanding and Setection 14.1 14.2 Descriptive Statistics Data Types 97 97 102 103 103 107
Contents vìi 14.3 14.4 Evaluations of Feature Content MissingValues 14.5 Exercises 113 15. Build Candidate Models 114 15.1 StartingtheProcess 15.2 Advanced Options 15.3 StartingtheAnalyticalProcess 15.4 Model Selection Process 15.4.1 Tournament Round 1:32% Sample 15.4.2 Tournament Round 2:64% Sample 15.4.3 TournamentRound3:CrossValidation 15.4.4 Tournament Round 4: Blending 15.5 Exercises 16. Understandingthe Process 16.1 16.2 16.3 16.4 16.5 114 116 121 127 128 131 131 132 133 134 LearningCurvesand Speed Accuraсу Tradeoffs Blueprints 16.3.1 NumericDataCleansmg(lmputation) 16.3.2 Standardization 16.3.3 One-hot Encoding 16.3.4 Ordinal Encoding 134 138 139 140 142 143 147 16.3.5 MatrixofWord-gramOccurrences 16.3.6 Classification Hyperparameter Optimization (Advanced Content) Exercises 149 151 154 156 17. Evaluate Model Performance 17.1 Introduction 17.2 ASampleAlgorithmandModel 17.3 ROC Curve 17.4 UsingtheLiftChartandProfitCurveforBusmessDecisions 17.5 Exercises 18. Comparing Model Pairs 18.1 18.2 110 112 Model Comparison PrioritizingModelingCriteriaandSelectingaModel 18.3 Exercises 157 157 159 164 176 179 180 180 185 187 SECTION V: INTERPRET AND COMMUNICATE 19. Interpret Model 19.1 Feature Impactson Target 19.2 The Overall impact of Features on the Target without ConsiderationofOtherFeatures 19.3 The Overall Impact of a Feature Adjusted for the Impact of Other Features 19.4 TheDirectionallmpactofFeaturesonTarget 19.5 ThePartiallmpactofFeaturesonTarget 191 191 192 193 194 195
viii Contents 19.6 ThePowerofLanguage 19.7 19.8 Hotspots Prediction Explanations 19.9 Exercises 20. Communicate Model Insights 198 201 203 205 206 20.1 Unlocking Holdout 20.2 Business Problem First 20.3 Pre-processing and Model Quality Metrics 20.4 Areas Where the Model Struggles 20.5 Most Predictive Features 20.6 NotAIIFeaturesAreCreatedEqual 207 209 210 213 214 214 20.7 20.8 217 218 Recommended BusinessActions Exercises SECTION VI: IMPLEMENT, DOCUMENT AND MAINTAIN 21. Set Up Prediction System 21.1 Retraining Model 21.2 21.3 Choose Deployment Strategy Exercises 22. Document Modeling Process for Reproducibility 22.1 22.2 Model Documentation Exercises 23. Create Model Monitoringand Maintenance Plan 23.1 23.2 23.3 Potential Problems Strategies Exercises 24. Seven Types of Target Leakage in Machine Learning and an Exercise 24.1 TypesofTargetLeakage 24.2 A Hands-on Exercise in DetectingTarget Leakage 24.3 Exercises 25. Time-Aware Modeling 25.1 An Example of Time-Aware Modeling 25.1.1 Problem Statement 25.1.2 Data 25.1.3 Initialize Analysis 25.1.4 Time-Aware Modeling Background 25.1.5 Data Preparation 25.1.6 Model Buildingand Residuals 25.1.7 Candidate Models 25.1.8 Selectingand Examininga Model 25.1.9 A Small Detour into Residuals 25.1.10 Model Value 25.1.11 LearningaboutAvocadoPriceDrivers 25.2 Exercises 221 221 222 227 228 228 229 230 230 230 232 233 233 236 239 240 240 240 241 241 241 244 247 247 249 253 256 256 258
Contents 26. Time-Series Modeling 26.1 The Assumptions of Time-Series Machine Learning 26.2 A Hands-on Exercise in Time-Series Analysis 26.2.1 Problem Context 26.2.2 Loading Data 26.2.3 SpecifyTimeUnitandGenerateFeatures 26.2.3 Examine Candidate Models 26.2.4 Digging into the Preferred Model 26.2.5 Predicting 26.3 Exercises 259 259 260 260 262 262 268 270 273 275 Appendix A. Datasets A.l Diabetes Patients Réadmissions Summary BusinessGoal Datasets Exercises Rights A.2 Luxury Shoes Summary 277 277 277 277 277 280 280 280 280 BusinessGoal Datasets Exercises A.3 Boston Airbnb Summary BusinessGoal Datasets Rights A.4 Part Backorders Summary BusinessGoal Datasets Exercises Rights A.5 Student Grades Portuguese Summary 281 281 283 283 283 284 284 287 287 287 287 287 288 289 289 289 BusinessGoal Datasets Exercises 289 289 290 Rights A.6 LendingClub Summary 293 293 293 BusinessGoal Dataset Rights A.7 CollegeStartingSalaries Summary 294 294 300 300 300 BusinessGoal Datasets Exercises 300 300 301 Rights 301
x Contents A.8 HRAttrition Summary Business Goal Datasets Exercises Rights A.9 Avocadopocalypse Now? Summary Business Goal Datasets Exercises Rights AppendixB. Optimization and Sorting Measures Appendixe. More on Cross Validation ЗОЇ ЗОЇ ՅՕշ ՅՕշ 304 305 305 305 3О6 3О6 307 307 ՅՕ8 311 References Index 315 319 |
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| illustrated | Illustrated |
| index_date | 2024-07-03T17:47:29Z |
| indexdate | 2024-08-06T09:56:03Z |
| institution | BVB |
| isbn | 9780190941659 9780190941666 |
| language | English |
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| oclc_num | 1231450020 |
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| publishDate | 2021 |
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| spellingShingle | Larsen, Kai R. Becker, Daniel S. Automated machine learning for business Maschinelles Lernen (DE-588)4193754-5 gnd Automation (DE-588)4003957-2 gnd Data Science (DE-588)1140936166 gnd Unternehmen (DE-588)4061963-1 gnd |
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| title | Automated machine learning for business |
| title_auth | Automated machine learning for business |
| title_exact_search | Automated machine learning for business |
| title_exact_search_txtP | Automated machine learning for business |
| title_full | Automated machine learning for business Kai R. Larsen and Daniel S. Becker |
| title_fullStr | Automated machine learning for business Kai R. Larsen and Daniel S. Becker |
| title_full_unstemmed | Automated machine learning for business Kai R. Larsen and Daniel S. Becker |
| title_short | Automated machine learning for business |
| title_sort | automated machine learning for business |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd Automation (DE-588)4003957-2 gnd Data Science (DE-588)1140936166 gnd Unternehmen (DE-588)4061963-1 gnd |
| topic_facet | Maschinelles Lernen Automation Data Science Unternehmen |
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Inhaltsverzeichnis
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