In base R, you can fit one using the lmmethod. Linear regression is one of the most well known algorithms. Our first caret example consists of fitting a simple linear regression. I’ll do a small tweak to make the iris problem a binary one (unfortunately glm, the logistic regression implementation in R doesn’t support multiclass problems): iris$target <- ifelse( iris$Species = 'setosa', 1, 0 )Īs we’ll want to evaluate the predict method, let’s split our two dataframes into train and test first - I’ll use a personal favorite, caTools: library(caTools) # Train Test Split on both Iris and Mtcars train_test_split <- function(df) # Unwrapping mtcars mtcars_train <- train_test_split(mtcars)] mtcars_test <- train_test_split(mtcars)] # Unwrapping iris iris_train <- train_test_split(iris)] iris_test <- train_test_split(iris)] The mtcars dataset that will be used as our regression task.The iris dataset, a very well known dataset that will represent our classification task.We’ll wrap everything by checking how the predict works with different caretmodels.įor simplicity, and because we want to focus on the library itself, we’ll use two of the most famous toy datasets available in R:.Experimenting with our own hyperparameter tuning.Then, we’ll learn how to setup our own custom cross-validation function followed by tweaking our algorithms for different optimization metrics.
With each algorithm we’ll train, we’ll be exposed to new concepts such as hyperparameter tuning, cross-validation, factors and other meaningful details. We’ll start by learning how to train different models by changing the method argument.In this guide, we are going to explore the package in four different dimensions: Adding to the flexibility, we get embedding hyperparameter tuning and cross validation - two techniques that will improve our algorithm’s generalization power. This layer of abstraction provides a common interface to train models in R, just by tweaking an argument - the method.Ĭaret(for Classification and Regression Training) is one of the most popular machine learning libraries in R. With flexibility as its main feature, caretenables you to train different types of algorithms using a simple trainfunction. Photo by Heidi Fin is a pretty powerful machine learning library in R.
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