RobustPrediction (version 0.1.7)

tuneandtrainIntRidge: Tune and Train Internal Ridge

Description

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

Usage

tuneandtrainIntRidge(
  data,
  maxit = 120000,
  nlambda = 200,
  nfolds = 5,
  seed = 123
)

Value

A list containing the best lambda value (`best_lambda`) and the final trained model (`best_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.

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 200.

nfolds

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

seed

An integer specifying the random seed for reproducibility. Default is 123.

Details

The function trains a logistic Ridge regression model on the training dataset and performs cross-validation to select the best lambda value. The lambda value that gives the highest AUC on the training dataset during cross-validation is chosen as the best model.

Examples

# Load sample data
data(sample_data_train)

# Example usage
result <- tuneandtrainIntRidge(sample_data_train, maxit = 120000, 
  nlambda = 200, nfolds = 5, seed = 123)
result$best_lambda
result$best_model