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I. Introduction
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I. Introduction
by Max Kuhn, Julia Silge
Tidy Modeling with R
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
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1. Software for Modeling
Part I.
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