Get going with tidymodels, a collection of R packages for modeling and machine learning. Whether you're just starting out or have years of experience with modeling, this practical introduction shows data analysts, business analysts, and data scientists how the tidymodels framework offers a consistent, flexible approach for your work.

RStudio engineers Max Kuhn and Julia Silge demonstrate ways to create models by focusing on an R dialect called the tidyverse. Software that adopts tidyverse principles shares both a high-level design philosophy and low-level grammar and data structures, so learning one piece of the ecosystem makes it easier to learn the next. You'll understand why the tidymodels framework has been built to be used by a broad range of people.

With this book, you will:

• Learn the steps necessary to build a model from beginning to end
• Understand how to use different modeling and feature engineering approaches fluently
• Examine the options for avoiding common pitfalls of modeling, such as overfitting
• Learn practical methods to prepare your data for modeling
• Tune models for optimal performance
• Use good statistical practices to compare, evaluate, and choose among models

Table of Contents

Part I. Introduction

Chapter 1. Software for Modeling

Chapter 2. A Tidyverse Primer

Chapter 3. A Review of R Modeling Fundamentals

Part II. Modeling Basics

Chapter 4. The Ames Housing Data

Chapter 5. Spending Our Data

Chapter 6. Fitting Models with parsnip

Chapter 7. A Model Workflow

Chapter 8. Feature Engineering with Recipes Chapter 9. Judging Model Effectiveness

Part III. Tools for Creating Effective Models

Chapter 10. Resampling for Evaluating Performance

Chapter 11. Comparing Models with Resampling

Chapter 12. Model Tuning and the Dangers of Overfitting

Chapter 13. Grid Search

Chapter 14. Iterative Search

Chapter 15. Screening Many Models

Part IV. Beyond the Basics

Chapter 16. Dimensionality Reduction

Chapter 17. Encoding Categorical Data

Chapter 18. Explaining Models and Predictions

Chapter 19. When Should You Trust Your Predictions?

Chapter 20. Ensembles of Models

Chapter 21. Inferential Analysis

Welcome to Tidy Modeling with R! This book is a guide to using a collection of software in the R programming language for model building called tidymodels, and it has two main goals:

• First and foremost, this book provides a practical introduction to how to use these specific R packages to create models. We focus on a dialect of R called the tidyverse that is designed with a consistent, human-centered philosophy and demonstrate how the tidyverse and the tidymodels packages can be used to produce high quality statistical and machine learning models.
• Second, this book will show you how to develop good methodology and statistical practices. Whenever possible, our software, documentation, and other materials attempt to prevent common pitfalls.

About the Author

Max Kuhn is a software engineer at RStudio, where he works to improve R’s modeling capabilities. He’s applied models in the pharmaceutical and diagnostic industries for more than 18 years.

Julia Silge is a software engineer at RStudio working on open source modeling tools. With a PhD in astrophysics, she’s worked as a data scientist in tech and the nonprofit sector.