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.
이 연습은 강의의 일부입니다
Hyperparameter Tuning in R
연습 안내
- Test four different values for each hyperparameter with automatic tuning in
caret.
실습형 인터랙티브 연습
이 예제를 이 샘플 코드를 완성하여 풀어보세요.
# 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()