tuneandtrainInt {RobustPrediction} | R Documentation |
Tune and Train by tuning method Int
Description
This function tunes and trains a specified classifier using internal cross-validation. The classifier is specified by the 'classifier' argument, and the function delegates to the appropriate tuning and training function based on this choice.
Usage
tuneandtrainInt(data, classifier, ...)
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. |
classifier |
A character string specifying the classifier to use. Must be one of 'boosting', 'rf', 'lasso', 'ridge', 'svm'. |
... |
Additional arguments to pass to the specific classifier function. |
Value
A list containing the results from the specific classifier's tuning and training process. The list typically includes:
-
best_hyperparams
: The best hyperparameters selected by cross-validation. -
best_model
: The final trained model using the selected hyperparameters. -
final_auc
: Cross-validation results (AUC).
Examples
# Load sample data
data(sample_data_train)
# Example usage with Lasso
result_lasso <- tuneandtrainInt(sample_data_train, classifier = "lasso",
maxit = 120000, nlambda = 100)
result_lasso$best_lambda
result_lasso$best_model
result_lasso$final_auc
result_lasso$active_set_Train
# Example usage with Ridge
result_ridge <- tuneandtrainInt(sample_data_train, classifier = "ridge",
maxit = 120000, nlambda = 100)
result_ridge$best_lambda
result_ridge$best_model
result_ridge$final_auc