Tidy Modeling with R (Final Release) 1 / converted Edition by Max Kuhn 9781492096443 149209644X

Tidy Modeling with R (Final Release) 1 / converted Edition Max Kuhn – Ebook Instant Download/Delivery ISBN(s): 9781492096481, 9781492096474, 1492096482, 1492096474, 9781492096443, 149209644X

Product details:

• ISBN 10: 149209644X
• ISBN 13: 9781492096443
• Author: Max Kuhn

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 contents:

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

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