Course
Dimensionality Reduction in R
BasicSkill Level
Updated 12/2024RMachine Learning4 hr16 videos56 Exercises4,600 XP2,696Statement of Accomplishment
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Why learn dimensionality reduction?
We live in the information age—an era of information overload. The art of extracting essential information from data is a marketable skill. Models train faster on reduced data. In production, smaller models mean faster response time. Perhaps most important, smaller data and models are often easier to understand. Dimensionality reduction is your Occam’s razor in data science.
What will you learn in this course?
The difference between feature selection and feature extraction! Using R, you will learn how to identify and remove features with low or redundant information, keeping the features with the most information. That’s feature selection. You will also learn how to extract combinations of features as condensed components that contain maximal information. That’s feature extraction!
But most importantly, using R’s new tidymodel package, you will use real-world data to build models with fewer features without sacrificing significant performance.
Prerequisites
Modeling with tidymodels in R1
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.
2
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.
3
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.
4
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.
Dimensionality Reduction in R
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FAQs
Is this course appropriate for someone new to machine learning in R?
Yes. It is listed as beginner level and assumes familiarity with tidyverse basics and tidymodels rather than advanced machine learning experience.
What dimensionality reduction techniques are covered?
You learn feature selection methods like variance and correlation filtering, lasso and random forest selection, and feature extraction techniques including PCA, t-SNE, and UMAP.
Does the course use the tidymodels framework?
Yes. You build tidymodels recipes and workflows throughout the course to select and extract features as part of a reproducible model-building pipeline.
What is the difference between feature selection and feature extraction?
Feature selection removes low-information variables, while feature extraction creates new condensed components like principal components that combine information from multiple original features.
Will reducing features hurt my model performance?
The course teaches you how to simplify data while maintaining strong predictive performance, showing that removing redundant features often improves model speed and interpretability.
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