Machine Learning with Caret

We’ll demonstrate a basic machine learning workflow using the caret package for building a predictive model.

Step 1: Install and Load Caret

If you don’t have caret installed, you can do so with:

rCopy codeinstall.packages("caret")

Then, load the library:

rCopy codelibrary(caret)

Step 2: Create a Sample Dataset

We’ll use the same dataset but add a binary outcome variable to predict:

rCopy code# Adding a binary outcome variable
set.seed(789)
data$outcome <- ifelse(data$weight > 70, "Heavy", "Light")

Step 3: Split the Dataset

Split the data into training and testing sets:

rCopy code# Set seed for reproducibility
set.seed(123)

# Create a training index
train_index <- createDataPartition(data$outcome, p = 0.7, list = FALSE)

# Split data into training and testing sets
train_data <- data[train_index, ]
test_data <- data[-train_index, ]

Step 4: Train a Model

We’ll train a simple logistic regression model:

rCopy code# Train a logistic regression model
model <- train(outcome ~ height + weight, data = train_data, method = "glm", family = "binomial")

# Print the model summary
summary(model)

Step 5: Make Predictions

Use the model to make predictions on the test set:

rCopy code# Make predictions on the test set
predictions <- predict(model, newdata = test_data)

# Confusion matrix to evaluate performance
confusionMatrix(predictions, test_data$outcome)