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
You might also like
book
Data Mesh
We're at an inflection point in data, where our data management solutions no longer match the …
book
Deep Learning for Coders with fastai and PyTorch
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. …
book
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition
Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. …
book
Designing Machine Learning Systems
Machine learning systems are both complex and unique. Complex because they consist of many different components …