tuneandtrainExtRF {RobustPrediction} | R Documentation |
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. Themin.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)
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 ( |
num.trees |
An integer specifying the number of trees in the Random Forest. Default is 500. |
Value
A list containing the following components:
-
best_min_node_size
: The optimalmin.node.size
value determined during the tuning process. -
best_model
: The trained Random Forest model using the selectedmin.node.size
. -
est_auc
: The AUC value evaluated on the external dataset. This is only returned whenestperf = TRUE
, providing a conservative (slightly pessimistic) estimate of the model's performance.
Examples
# 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