How to use Different Algorithms using Caret Package in R (original) (raw)

Last Updated : 26 Jun, 2025

The caret (Classification And Regression Training) package in R provides a unified framework for training, tuning and evaluating a wide range of machine learning algorithms.

Installing and Loading the caret Package

We will install caret and load it along with any other necessary dependencies.

R `

install.packages("caret") library(caret)

`

Preparing the Data

We will be using the iris data set which is a built-in dataset in R Language. We will load the data and set a random seed and use **createDataPartition() to split into 80% training and 20% testing to evaluate performance on unseen observations.

R `

set.seed(123) data(iris)

idx <- createDataPartition(iris$Species, p = 0.8, list = FALSE) train_data <- iris[idx, ] test_data <- iris[-idx, ]

`

Classification Algorithms

There are many classification algorithms available in Caret package. We will define cross-validation, implement the model and evaluate the model on test data.

1. Random Forest

Random Forest aggregates many decision trees to reduce overfitting and improve accuracy.

R `

install.packages("randomForest") library(randomForest)

ctrl <- trainControl(method = "cv", number = 10)

rf_model <- train(Species ~ ., data = train_data, method = "rf", trControl = ctrl)

rf_preds <- predict(rf_model, test_data) confusionMatrix(rf_preds, test_data$Species)

`

**Output:

random forest

2. CART (Decision Tree)

CART builds a single tree and prunes it based on the complexity parameter (cp).

R `

install.packages("rpart") library(rpart)

cart_model <- train(Species ~ ., data = train_data, method = "rpart", trControl = ctrl)

cart_preds <- predict(cart_model, test_data) confusionMatrix(cart_preds, test_data$Species)

`

**Output:

decision tree

3. k-Nearest Neighbors (k-NN)

k-NN classifies observations based on the majority vote of their k nearest neighbors.

R `

knn_grid <- expand.grid(k = seq(3, 15, by = 2))

knn_model <- train(Species ~ ., data = train_data, method = "knn", trControl = ctrl, tuneGrid = knn_grid)

knn_preds <- predict(knn_model, test_data) confusionMatrix(knn_preds, test_data$Species)

`

**Output:

knn

4. Support Vector Machine (SVM)

SVM separates classes by finding the hyperplane with maximum margin.

R `

install.packages("kernlab") library(kernlab)

svm_model <- train(Species ~ ., data = train_data, method = "svmRadial", trControl = ctrl, preProcess = c("center", "scale"))

svm_preds <- predict(svm_model, test_data) confusionMatrix(svm_preds, test_data$Species)

`

**Output:

svm

Regression Algorithms

There are many classification algorithms available in Caret package. We will define cross-validation and summarise the model and evaluate the model using Root mean squared error (RMSE).

Data Split for Regression

We’ll use the mtcars dataset to demonstrate regression examples.

R `

set.seed(456) data(mtcars)

idx2 <- createDataPartition(mtcars$mpg, p = 0.8, list = FALSE) train_reg <- mtcars[idx2, ] test_reg <- mtcars[-idx2, ]

`

1. Linear Regression

Ordinary least squares provides a baseline for regression.

R `

ctrl_reg <- trainControl(method = "cv", number = 5)

lm_model <- train(mpg ~ ., data = train_reg, method = "lm", trControl = ctrl_reg)

print(lm_model)

lm_preds <- predict(lm_model, test_reg) RMSE(lm_preds, test_reg$mpg)

`

**Output:

linear regression

2. Random Forest Regression

Random Forest Regression is an ensemble method that builds multiple decision trees and combines their results to improve accuracy and reduce overfitting.

R `

rf_reg <- train(mpg ~ ., data = train_reg, method = "rf", trControl = ctrl_reg) print(rf_reg)

rf_reg_preds <- predict(rf_reg, test_reg) RMSE(rf_reg_preds, test_reg$mpg)

`

**Output:

Random Forest Regressor

In this article, we demonstrated how to train and evaluate different classification and regression algorithms using the caret package in R, providing a consistent framework for model building, tuning and comparison.