Dimensionality Reduction in R

Prepare to simplify large data sets! You will learn about information, how to assess feature importance, and practice identifying low-information features. By the end of the chapter, you will understand the difference between feature selection and feature extraction—the two approaches to dimensionality reduction.

Learn how to identify information-rich and information-poor features missing value ratios, variance, and correlation. Then you'll discover how to build tidymodel recipes to select features using these information indicators.

Chapter three introduces the difference between unsupervised and supervised feature selection approaches. You'll review how to use tidymodels workflows to build models. Then, you'll perform supervised feature selection using lasso regression and random forest models.

In this final chapter, you'll gain a strong intuition of feature extraction by understanding how principal components extract and combine the most important information from different features. Then learn about and apply three types of feature extraction — principal component analysis (PCA), t-SNE, and UMAP. Discover how you can use these feature extraction methods as a preprocessing step in the tidymodels model-building process.