Feature Engineering and Selection: A Practical Approach for Predictive Models (Chapman & Hall/CRC Data Science Series)

308 Pages·2019·46.111 MB·English

by  Max Kuhn, Kjell Johnson

Feature Engineering and
Selection
A Practical Approach for Predictive
Models
CHAPMAN & HALL/CRC DATA SCIENCE SERIES
Reflecting the interdisciplinary nature of the field, this book series brings together
researchers, practitioners, and instructors from statistics, computer science, machine
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tions, and textbooks in data science.
The inclusion of concrete examples, applications, and methods is highly encouraged.
The scope of the series includes titles in the areas of machine learning, pattern recog-
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Published Titles
Feature Engineering and Selection: A Practical Approach for Predictive Models
Max Kuhn and Kjell Johnson
Probability and Statistics for Data Science: Math + R + Data
Norman Matloff
Feature Engineering and
Selection
A Practical Approach for Predictive
Models
Max Kuhn
Kjell Johnson
Map tiles on front cover: © OpenStreetMap © CartoDB.
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To:
Maryann Kuhn
Valerie, Truman, and Louie
Contents
Preface • xi
Author Bios • xv
1 Introduction • 1
1.1 A Simple Example • 4
1.2 Important Concepts • 7
1.3 A More Complex Example • 15
1.4 Feature Selection • 17
1.5 An Outline of the Book • 18
1.6 Computing • 20
2 Illustrative Example: Predicting Risk of Ischemic
Stroke • 21
2.1 Splitting • 23
2.2 Preprocessing • 23
2.3 Exploration • 26
2.4 Predictive Modeling across Sets • 30
2.5 Other Considerations • 34
2.6 Computing • 34
3 A Review of the Predictive Modeling Process • 35
3.1 Illustrative Example: OkCupid Profile Data • 35
3.2 Measuring Performance • 36
3.3 Data Splitting • 46
3.4 Resampling • 47
3.5 Tuning Parameters and Overfitting • 56
3.6 Model Optimization and Tuning • 57
3.7 Comparing Models Using the Training Set • 61
3.8 Feature Engineering without Overfitting • 62
3.9 Summary • 64
3.10 Computing • 64
4 Exploratory Visualizations • 65
4.1 Introduction to the Chicago Train Ridership Data • 66
vii
viii Contents
4.2 Visualizations for Numeric Data: Exploring Train Ridership Data • 69
4.3 Visualizations for Categorical Data: Exploring the OkCupid Data • 83
4.4 Postmodeling Exploratory Visualizations • 88
4.5 Summary • 92
4.6 Computing • 92
5 Encoding Categorical Predictors • 93
5.1 Creating Dummy Variables for Unordered Categories • 94
5.2 Encoding Predictors with Many Categories • 96
5.3 Approaches for Novel Categories • 102
5.4 Supervised Encoding Methods • 102
5.5 Encodings for Ordered Data • 107
5.6 Creating Features from Text Data • 109
5.7 Factors versus Dummy Variables in Tree-Based Models • 114
5.8 Summary • 119
5.9 Computing • 120
6 Engineering Numeric Predictors • 121
6.1 1:1 Transformations • 122
6.2 1:Many Transformations • 126
6.3 Many:Many Transformations • 133
6.4 Summary • 154
6.5 Computing • 155
7 Detecting Interaction Effects • 157
7.1 Guiding Principles in the Search for Interactions • 161
7.2 Practical Considerations • 164
7.3 The Brute-Force Approach to Identifying Predictive Interactions • 165
7.4 Approaches when Complete Enumeration Is Practically Impossible • 172
7.5 Other Potentially Useful Tools • 184
7.6 Summary • 185
7.7 Computing • 186
8 Handling Missing Data • 187
8.1 Understanding the Nature and Severity of Missing Information • 189
8.2 Models that Are Resistant to Missing Values • 195
8.3 Deletion of Data • 196
8.4 Encoding Missingness • 197
8.5 Imputation Methods • 198
8.6 Special Cases • 203
8.7 Summary • 203
8.8 Computing • 204
9 Working with Profile Data • 205
9.1 Illustrative Data: Pharmaceutical Manufacturing Monitoring • 209
9.2 What Are the Experimental Unit and the Unit of Prediction? • 210
9.3 Reducing Background • 214
9.4 Reducing Other Noise • 215
9.5 Exploiting Correlation • 217
Contents ix
9.6 Impacts of Data Processing on Modeling • 219
9.7 Summary • 224
9.8 Computing • 225
10 Feature Selection Overview • 227
10.1 Goals of Feature Selection • 227
10.2 Classes of Feature Selection Methodologies • 228
10.3 Effect of Irrelevant Features • 232
10.4 Overfitting to Predictors and External Validation • 235
10.5 A Case Study • 238
10.6 Next Steps • 240
10.7 Computing • 240
11 Greedy Search Methods • 241
11.1 Illustrative Data: Predicting Parkinson’s Disease • 241
11.2 Simple Filters • 241
11.3 Recursive Feature Elimination • 248
11.4 Stepwise Selection • 252
11.5 Summary • 254
11.6 Computing • 255
12 Global Search Methods • 257
12.1 Naive Bayes Models • 257
12.2 Simulated Annealing • 260
12.3 Genetic Algorithms • 270
12.4 Test Set Results • 280
12.5 Summary • 281
12.6 Computing • 282
Bibliography • 283
Index • 295