Feature Selection with the Caret R Package

Last Updated on August 22, 2019

Selecting the right features in your data can mean the difference between mediocre performance with long training times and great performance with short training times.

The caret R package provides tools to automatically report on the relevance and importance of attributes in your data and even select the most important features for you.

In this post you will discover the feature selection tools in the Caret R package with standalone recipes in R.

After reading this post you will know:

• How to remove redundant features from your dataset.
• How to rank features in your dataset by their importance.
• How to select features from your dataset using the Recursive Feature Elimination method.

Kick-start your project with my new book Machine Learning Mastery With R, including step-by-step tutorials and the R source code files for all examples.

Let’s get started.

Confidence Intervals for Machine Learning
Photo by Paul Balfe, some rights reserved.