Tidy Modeling with R

Spis treści ebooka

• Preface
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly 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 ThousandBeans
  • 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

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