## Book description

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

## Publisher resources

## Table of contents

- Preface
- I. Introduction
- 1. Software for Modeling
- 2. A Tidyverse Primer
- 3. A Review of R Modeling Fundamentals
- II. Modeling Basics
- 4. The Ames Housing Data
- 5. Spending Our Data
- 6. Fitting Models with parsnip
- 7. A Model Workflow
- 8. Feature Engineering with Recipes
- 9. Judging Model Effectiveness
- III. Tools for Creating Effective Models
- 10. Resampling for Evaluating Performance
- 11. Comparing Models with Resampling
- 12. Model Tuning and the Dangers of Overfitting
- 13. Grid Search
- 14. Iterative Search
- 15. Screening Many Models
- IV. Beyond the Basics
- 16. Dimensionality Reduction
- 17. Encoding Categorical Data
- 18. Explaining Models and Predictions
- 19. When Should You Trust Your Predictions?
- 20. Ensembles of Models
- 21. Inferential Analysis
- A. Recommended Preprocessing
- References
- Index
- About the Authors

## Product information

- Title: Tidy Modeling with R
- Author(s):
- Release date: July 2022
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781492096481

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