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

Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Preface
Author Bios
1: Introduction
	1.1 A Simple Example
	1.2 Important Concepts
	1.3 A More Complex Example
	1.4 Feature Selection
	1.5 An Outline of the Book
	1.6 Computing
2: Illustrative Example: Predicting Risk of Ischemic Stroke
	2.1 Splitting
	2.2 Preprocessing
	2.3 Exploration
	2.4 Predictive Modeling across Sets
	2.5 Other Considerations
	2.6 Computing
3: A Review of the Predictive Modeling Process
	3.1 Illustrative Example: OkCupid Profile Data
	3.2 Measuring Performance
	3.3 Data Splitting
	3.4 Resampling
	3.5 Tuning Parameters and Overfitting
	3.6 Model Optimization and Tuning
	3.7 Comparing Models Using the Training Set
	3.8 Feature Engineering without Overfitting
	3.9 Summary
	3.10 Computing
4: Exploratory Visualizations
	4.1 Introduction to the Chicago Train Ridership Data
	4.2 Visualizations for Numeric Data: Exploring Train Ridership Data
	4.3 Visualizations for Categorical Data: Exploring the OkCupid Data
	4.4 Postmodeling Exploratory Visualizations
	4.5 Summary
	4.6 Computing
5: Encoding Categorical Predictors
	5.1 Creating Dummy Variables for Unordered Categories
	5.2 Encoding Predictors with Many Categories
	5.3 Approaches for Novel Categories
	5.4 Supervised Encoding Methods
	5.5 Encodings for Ordered Data
	5.6 Creating Features from Text Data
	5.7 Factors versus Dummy Variables in Tree-Based Models
	5.8 Summary
	5.9 Computing
6: Engineering Numeric Predictors
	6.1 1:1 Transformations
	6.2 1:Many Transformations
	6.3 Many:Many Transformations
	6.4 Summary
	6.5 Computing
7: Detecting Interaction Effects
	7.1 Guiding Principles in the Search for Interactions
	7.2 Practical Considerations
	7.3 The Brute-Force Approach to Identifying Predictive Interactions
	7.4 Approaches when Complete Enumeration Is Practically Impossible
	7.5 Other Potentially Useful Tools
	7.6 Summary
	7.7 Computing
8: Handling Missing Data
	8.1 Understanding the Nature and Severity of Missing Information
	8.2 Models that Are Resistant to Missing Values
	8.3 Deletion of Data
	8.4 Encoding Missingness
	8.5 Imputation Methods
	8.6 Special Cases
	8.7 Summary
	8.8 Computing
9: Working with Profile Data
	9.1 Illustrative Data: Pharmaceutical Manufacturing Monitoring
	9.2 What Are the Experimental Unit and the Unit of Prediction?
	9.3 Reducing Background
	9.4 Reducing Other Noise
	9.5 Exploiting Correlation
	9.6 Impacts of Data Processing on Modeling
	9.7 Summary
	9.8 Computing
10: Feature Selection Overview
	10.1 Goals of Feature Selection
	10.2 Classes of Feature Selection Methodologies
	10.3 Effect of Irrelevant Features
	10.4 Overfitting to Predictors and External Validation
	10.5 A Case Study
	10.6 Next Steps
	10.7 Computing
11: Greedy Search Methods
	11.1 Illustrative Data: Predicting Parkinson’s Disease
	11.2 Simple Filters
	11.3 Recursive Feature Elimination
	11.4 Stepwise Selection
	11.5 Summary
	11.6 Computing
12: Global Search Methods
	12.1 Naive Bayes Models
	12.2 Simulated Annealing
	12.3 Genetic Algorithms
	12.4 Test Set Results
	12.5 Summary
	12.6 Computing
Bibliography
Index