Feature Engineering in R: Tidymodels & Recipes Package

Feature Engineering in R: Tidymodels & Recipes Package

Korshub's take

The Korshub Take

This intermediate R course introduces principled feature engineering using the modern tidymodels ecosystem. Focusing on the recipes package, it shows how domain-informed transformations improve model interpretability, speed up training, and reduce overfitting.

Who It's For

R programmers and data scientists who want to build reproducible, robust data preprocessing pipelines using tidymodels.

Key Takeaways

The Recipes Framework: Master the R recipes package to create, transform, and select feature sets seamlessly.
Dimensionality & Transformation: Apply variable selection, transformations, and feature extraction to streamline complex datasets.
Overfitting Prevention: Implement sound engineering principles that improve algorithm speed while preventing data leakage and overfitting.

About this course

Discover Feature Engineering for Machine Learning

In this course, you’ll learn about feature engineering, which is at the heart of many times of machine learning models. As the performance of any model is a direct consequence of the features it’s fed, feature engineering places domain knowledge at the center of the process. You’ll become acquainted with principles of sound feature engineering, helping to reduce the number of variables where possible, making learning algorithms run faster, improving interpretability, and preventing overfitting.

Implement Feature Engineering Techniques in R

You will learn how to implement feature engineering techniques using the R tidymodels framework, emphasizing the recipe package that will allow you to create, extract, transform, and select the best features for your model.

Engineer Features and Build Better ML Models

When faced with a new dataset, you will be able to identify and select relevant features and disregard non-informative ones to make your model run faster without sacrificing accuracy. You will also become comfortable applying transformations and creating new features to make your models more efficient, interpretable, and accurate!

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