Dimensionality Reduction in R

Overview

Learn dimensionality reduction techniques in R and master feature selection and extraction for your own data and models.

Do you ever work with datasets with an overwhelming number of features? In this course, you will learn dimensionality reduction techniques that will help you simplify your data and the models that you build with your data while maintaining good predictive performance. Dimensionality reduction is your Occam’s razor in data science. Using R, you will learn how to identify and remove features, how to extract combinations of features as condensed components that contain maximal information, and use real-world data to build models with fewer features without sacrificing significant performance.

Syllabus

Foundations of Dimensionality Reduction

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.

Feature Selection for Feature Importance

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.

Feature Selection for Model Performance

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.

Feature Extraction and Model Performance

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.