RobustPrediction (version 0.1.7)

tuneandtrainIntLasso: Tune and Train Internal Lasso

Description

This function tunes and trains a Lasso classifier using the glmnet package. The function performs internal cross-validation to evaluate a sequence of lambda (regularization) values and selects the best model based on the Area Under the Curve (AUC).

Usage

tuneandtrainIntLasso(data, maxit = 120000, nlambda = 200, nfolds = 5)

Value

A list containing the best lambda value (`best_lambda`), the final trained model (`best_model`), and the number of active coefficients (`active_set_Train`).

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.

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 Lasso model. Default is 200.

nfolds

An integer specifying the number of folds for cross-validation. Default is 5.

Details

This function trains a logistic Lasso model on the training dataset using cross-validation. The lambda value that results in the highest AUC during cross-validation is chosen as the best model, and the final model is trained on the full training dataset with this optimal lambda value.

Examples

# Load sample data
data(sample_data_train)

# Example usage
result <- tuneandtrainIntLasso(sample_data_train, maxit = 120000, nlambda = 200, nfolds = 5)
result$best_lambda
result$best_model
result$active_set_Train