Here’s a basic plot with a couple of our variables: featurePlot(x = penguins, Max Kuhn’s The caret` Package Bookdown document provides some interesting examples of its functionality. As someone who primarily uses ggplot2 I found this function a bit difficult to use, but you may find it helpful still. It runs off of the lattice package, so if you are familiar with this method of plotting you might find it familiar. This package contains LTER data for three penguin species on islands in Antarctica.Ĭaret provides a function called featurePlot(), which is used to visualize datasets. It will be loaded automatically when you load the palmerpenguins package: head(penguins) We’ll use the penguins dataset from palmerpenguins. Start by loading the necessary packages: library(tidyverse) We’ll be working with data from the palmerpenguins package, and using the caret, and tidyverse packages. Both caret and tidymodels have Max Kuhn as a main author, but tidymodels aims to streamline ML for use with tidyverse packages. In addition to caret, there is also a group of packages referred to as tidymodels that is currently in development and which is also available for use.

It provides methods for common ML steps, such as pre-processing, training, tuning, and evaluating predictive models. The name caret stands for “Classification and Regression Training” according to the authors.

The caret R package has been a staple of machine learning (ML) methods in R for a long time.

Caret r package