RobustPrediction (version 0.1.7)

tuneandtrainExt: Tune and Train Classifier by Tuning Method Ext

Description

This function tunes and trains a classifier using an external validation dataset. Based on the specified classifier, the function selects and runs the appropriate tuning and training process. The external validation data is used to optimize the model's hyperparameters and improve generalization performance across datasets.

Usage

tuneandtrainExt(data, dataext, classifier, ...)

Value

A list containing the results from the classifier's tuning and training process. The returned object typically includes:

• best_model: The final trained model using the best hyperparameters.

• best_hyperparams: The optimal hyperparameters found during the tuning process.

• final_auc: Performance metrics (AUC) of the final model.

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. Ensure that the data is properly formatted, with no missing values.

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. The external data is used for tuning hyperparameters to avoid overfitting on the training data.

classifier

A character string specifying the classifier to use. Must be one of the following:

• "boosting" for gradient boosting models.

• "rf" for Random Forest.

• "lasso" for Lasso regression (for feature selection and regularization).

• "ridge" for Ridge regression (for regularization).

• "svm" for Support Vector Machines (SVM).

...

Additional arguments to pass to the specific classifier function. These may include hyperparameters such as the number of trees for Random Forest, regularization parameters for Lasso/Ridge, or kernel settings for SVM.

Examples

# Load sample data
data(sample_data_train)
data(sample_data_extern)

# Example usage with Lasso
result_lasso <- tuneandtrainExt(sample_data_train, sample_data_extern, 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 <- tuneandtrainExt(sample_data_train, sample_data_extern, 
  classifier = "ridge", maxit = 120000, nlambda = 100)
result_ridge$best_lambda
result_ridge$best_model
result_ridge$final_auc