tune_rf_model {text2emotion} | R Documentation |
Tune Random Forest Model Hyperparameters
Description
This function performs hyperparameter tuning for a Random Forest model using grid search. It searches over the grid of 'mtry' (number of variables to consider at each split) and 'ntree' (number of trees in the forest) to find the best model based on training accuracy.
Usage
tune_rf_model(
train_matrix,
train_labels,
mtry_grid = c(5, 10, 20),
ntree_grid = c(100, 200, 300),
seed = 123,
verbose = TRUE
)
Arguments
train_matrix |
A sparse matrix (class 'dgCMatrix') representing the training feature data. |
train_labels |
A factor vector representing the training labels. |
mtry_grid |
A vector of values to search for the 'mtry' parameter (number of variables to consider at each split). Default is 'c(5, 10, 20)'. |
ntree_grid |
A vector of values to search for the 'ntree' parameter (number of trees in the forest). Default is 'c(100, 200, 300)'. |
seed |
A seed value for reproducibility. Default is '123'. |
verbose |
A logical indicating whether to print progress information during the grid search. Default is 'TRUE'. |
Details
The function trains multiple Random Forest models using different combinations of 'mtry' and 'ntree' values, and evaluates their performance based on training accuracy. The hyperparameters that give the highest accuracy are returned as the best parameters. The process uses the 'ranger' package for training the Random Forest model.
Value
A list containing the best hyperparameters ('mtry', 'ntree', and 'accuracy'):
'mtry': The best number of variables to consider at each split.
'ntree': The best number of trees in the forest.
'accuracy': The accuracy achieved by the model with the best hyperparameters.