目录 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
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