tuneandtrainExtRidge {RobustPrediction} | R Documentation |
Tune and Train External Ridge
Description
This function tunes and trains a Ridge classifier using the glmnet
package.
It provides two strategies for tuning the regularization parameter lambda
based on the estperf
argument:
When
estperf = FALSE
(default): Hyperparameters are tuned using the external validation dataset. Thelambda
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
tuneandtrainExtRidge(
data,
dataext,
estperf = FALSE,
maxit = 120000,
nlambda = 100
)
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 ( |
maxit |
An integer specifying the maximum number of iterations. Default is 120000. |
nlambda |
An integer specifying the number of lambda values to use in the Ridge model. Default is 100. |
Value
A list containing the following components:
-
best_lambda
: The optimallambda
value determined during the tuning process. -
best_model
: The trained Ridge model using the selectedlambda
. -
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 <- tuneandtrainExtRidge(sample_data_train, sample_data_extern, maxit = 120000, nlambda = 100)
print(result$best_lambda) # Optimal lambda
print(result$best_model) # Final trained model
# Note: est_auc is not returned when estperf = FALSE
# Example usage with internal tuning and external validation
result_internal <- tuneandtrainExtRidge(sample_data_train, sample_data_extern,
estperf = TRUE, maxit = 120000, nlambda = 100)
print(result_internal$best_lambda) # Optimal lambda
print(result_internal$best_model) # Final trained model
print(result_internal$est_auc) # AUC on external validation dataset