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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

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