Tidy Modeling with R

Preface
	Conventions Used in This Book
	Using Code Examples
	O’Reilly Online Learning
	How to Contact Us
	Acknowledgments
I. Introduction
1. Software for Modeling
	Fundamentals for Modeling Software
	Types of Models
		Descriptive Models
		Inferential Models
		Predictive Models
	Connections Between Types of Models
	Some Terminology
	How Does Modeling Fit into the Data Analysis Process?
	Chapter Summary
2. A Tidyverse Primer
	Tidyverse Principles
		Design for Humans
		Reuse Existing Data Structures
		Design for the Pipe and Functional Programming
	Examples of Tidyverse Syntax
	Chapter Summary
3. A Review of R Modeling Fundamentals
	An Example
	What Does the R Formula Do?
	Why Tidiness Is Important for Modeling
	Combining Base R Models and the Tidyverse
	The tidymodels Metapackage
	Chapter Summary
II. Modeling Basics
4. The Ames Housing Data
	Exploring Features of Homes in Ames
	Chapter Summary
5. Spending Our Data
	Common Methods for Splitting Data
	What About a Validation Set?
	Multilevel Data
	Other Considerations for a Data Budget
	Chapter Summary
6. Fitting Models with parsnip
	Create a Model
	Use the Model Results
	Make Predictions
	parsnip-Extension Packages
	Creating Model Specifications
	Chapter Summary
7. A Model Workflow
	Where Does the Model Begin and End?
	Workflow Basics
	Adding Raw Variables to the workflow()
	How Does a workflow() Use the Formula?
		Tree-Based Models
		Special Formulas and Inline Functions
	Creating Multiple Workflows at Once
	Evaluating the Test Set
	Chapter Summary
8. Feature Engineering with Recipes
	A Simple recipe() for the Ames Housing Data
	Using Recipes
	How Data Are Used by the recipe()
	Examples of Steps
		Encoding Qualitative Data in a Numeric Format
		Interaction Terms
		Spline Functions
		Feature Extraction
		Row Sampling Steps
		General Transformations
		Natural Language Processing
	Skipping Steps for New Data
	Tidy a recipe()
	Column Roles
	Chapter Summary
9. Judging Model Effectiveness
	Performance Metrics and Inference
	Regression Metrics
	Binary Classification Metrics
	Multiclass Classification Metrics
	Chapter Summary
III. Tools for Creating Effective Models
10. Resampling for Evaluating Performance
	The Resubstitution Approach
	Resampling Methods
		Cross-Validation
		Repeated Cross-Validation
		Leave-One-Out Cross-Validation
		Monte Carlo Cross-Validation
		Validation Sets
		Bootstrapping
		Rolling Forecasting Origin Resampling
	Estimating Performance
	Parallel Processing
	Saving the Resampled Objects
	Chapter Summary
11. Comparing Models with Resampling
	Creating Multiple Models with Workflow Sets
	Comparing Resampled Performance Statistics
	Simple Hypothesis Testing Methods
	Bayesian Methods
		A Random Intercept Model
		The Effect of the Amount of Resampling
	Chapter Summary
12. Model Tuning and the Dangers of Overfitting
	Model Parameters
	Tuning Parameters for Different Types of Models
	What Do We Optimize?
	The Consequences of Poor Parameter Estimates
	Two General Strategies for Optimization
	Tuning Parameters in tidymodels
	Chapter Summary
13. Grid Search
	Regular and Nonregular Grids
		Regular Grids
		Nonregular Grids
	Evaluating the Grid
	Finalizing the Model
	Tools for Creating Tuning Specifications
	Tools for Efficient Grid Search
		Submodel Optimization
		Parallel Processing
		Benchmarking Boosted Trees
		Access to Global Variables
		Racing Methods
	Chapter Summary
14. Iterative Search
	A Support Vector Machine Model
	Bayesian Optimization
		A Gaussian Process Model
		Acquisition Functions
		The tune_bayes() Function
	Simulated Annealing
		Simulated Annealing Search Process
		The tune_sim_anneal() Function
	Chapter Summary
15. Screening Many Models
	Modeling Concrete Mixture Strength
	Creating the Workflow Set
	Tuning and Evaluating the Models
	Efficiently Screening Models
	Finalizing a Model
	Chapter Summary
IV. Beyond the Basics
16. Dimensionality Reduction
	What Problems Can Dimensionality Reduction Solve?
	A Picture Is Worth a Thousand…Beans
	A Starter Recipe
	Recipes in the Wild
		Preparing a Recipe
		Baking the Recipe
	Feature Extraction Techniques
		Principal Component Analysis
		Partial Least Squares
		Independent Component Analysis
		Uniform Manifold Approximation and Projection
	Modeling
	Chapter Summary
17. Encoding Categorical Data
	Is an Encoding Necessary?
	Encoding Ordinal Predictors
	Using the Outcome for Encoding Predictors
		Effect Encodings in tidymodels
		Effect Encodings with Partial Pooling
	Feature Hashing
	More Encoding Options
	Chapter Summary
18. Explaining Models and Predictions
	Software for Model Explanations
	Local Explanations
	Global Explanations
	Building Global Explanations from Local Explanations
	Back to Beans!
	Chapter Summary
19. When Should You Trust Your Predictions?
	Equivocal Results
	Determining Model Applicability
	Chapter Summary
20. Ensembles of Models
	Creating the Training Set for Stacking
	Blend the Predictions
	Fit the Member Models
	Test Set Results
	Chapter Summary
21. Inferential Analysis
	Inference for Count Data
	Comparisons with Two-Sample Tests
	Log-Linear Models
	A More Complex Model
	More Inferential Analysis
	Chapter Summary
A. Recommended Preprocessing
References
Index
About the Authors