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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.
Introduction
Software for Modeling
A Tidyverse Primer
A Review of R Modeling Fundamentals
Modeling Basics
The Ames Housing Data
Spending Our Data
Fitting Models with parsnip
A Model Workflow
Feature Engineering with Recipes
Judging Model Effectiveness
Tools for Creating Effective Models
Resampling for Evaluating Performance
Comparing Models with Resampling
Model Tuning and the Dangers of Overfitting
Grid Search
Iterative Search
Screening Many Models
Beyond the Basics
Dimensionality Reduction
Encoding Categorical Data
Explaining Models and Predictions
When Should You Trust Your Predictions?
Ensembles of Models
Inferential Analysis