Get startedGet started for free

Changing the number of hyperparameters to tune

When we examine the model object closely, we can see that caret already did some automatic hyperparameter tuning for us: train automatically creates a grid of tuning parameters. By default, if p is the number of tuning parameters, the grid size is 3^p. But we can also specify the number of different values to try for each hyperparameter.

The data has again been preloaded as bc_train_data. The libraries caret and tictoc have also been preloaded.

This exercise is part of the course

Hyperparameter Tuning in R

View Course

Exercise instructions

  • Test four different values for each hyperparameter with automatic tuning in caret.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Set seed.
set.seed(42)
# Start timer.
tic()
# Train model.
gbm_model <- train(diagnosis ~ ., 
                   data = bc_train_data, 
                   method = "gbm", 
                   trControl = trainControl(method = "repeatedcv", number = 5, repeats = 3),
                   verbose = FALSE,
                   ___)
# Stop timer.
toc()
Edit and Run Code