RobustPrediction (version 0.1.7)

tuneandtrainExtRF: Tune and Train External Random Forest

Description

This function tunes and trains a Random Forest classifier using the ranger package. It provides two strategies for tuning the min.node.size parameter based on the estperf argument:

• When estperf = FALSE (default): Hyperparameters are tuned using the external validation dataset. The min.node.size value that gives the highest AUC on the external dataset is selected as the best model. However, no AUC value is returned in this case, as per best practices.

• When estperf = TRUE: Hyperparameters are tuned internally using the training dataset. The model is then validated on the external dataset to provide a conservative (slightly pessimistic) AUC estimate.

Usage

tuneandtrainExtRF(data, dataext, estperf = FALSE, num.trees = 500)

Value

A list containing the following components:

• best_min_node_size: The optimal min.node.size value determined during the tuning process.

• best_model: The trained Random Forest model using the selected min.node.size.

• est_auc: The AUC value evaluated on the external dataset. This is only returned when estperf = TRUE, providing a conservative (slightly pessimistic) estimate of the model's performance.

Arguments

data

A data frame containing the training data. The first column should be the response variable (factor), and the remaining columns should be the predictor variables.

dataext

A data frame containing the external validation data. The first column should be the response variable (factor), and the remaining columns should be the predictor variables.

estperf

A logical value indicating whether to use internal tuning with external validation (TRUE) or external tuning (FALSE). Default is FALSE.

num.trees

An integer specifying the number of trees in the Random Forest. Default is 500.

Examples

# \donttest{
# Load sample data
data(sample_data_train)
data(sample_data_extern)

# Example usage with external tuning (default)
result <- tuneandtrainExtRF(sample_data_train, sample_data_extern, num.trees = 500)
print(result$best_min_node_size)  # Optimal min.node.size
print(result$best_model)          # Trained Random Forest model
# Note: est_auc is not returned when estperf = FALSE

# Example usage with internal tuning and external validation
result_internal <- tuneandtrainExtRF(sample_data_train, sample_data_extern, 
  estperf = TRUE, num.trees = 500)
print(result_internal$best_min_node_size)  # Optimal min.node.size
print(result_internal$best_model)          # Trained Random Forest model
print(result_internal$est_auc)             # AUC on external validation dataset
# }