Preface
Part 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 Tiflyverse 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
Part 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 workflow0
How Does a workflow0 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
Part Ill. 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 Overntting
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 An