RobustPrediction (version 0.1.7)

tuneandtrainIntBoost: Tune and Train Internal Boosting

Description

This function tunes and trains a Boosting classifier using the mboost package. The function evaluates a sequence of boosting iterations on the training dataset using internal cross-validation and selects the best model based on the Area Under the Curve (AUC).

Usage

tuneandtrainIntBoost(data, mstop_seq = seq(5, 1000, by = 5), nu = 0.1)

Value

A list containing the best number of boosting iterations (`best_mstop`) and the final Boosting classifier 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.

mstop_seq

A numeric vector of boosting iterations to be evaluated. Default is a sequence from 5 to 1000 with a step of 5.

nu

A numeric value for the learning rate. Default is 0.1.

Details

This function performs K-fold cross-validation on the training dataset, where the number of boosting iterations (mstop) is tuned to maximize the AUC. The optimal number of boosting iterations is selected, and the final model is trained on the entire training dataset.

Examples

# \donttest{
# Load sample data
data(sample_data_train)

# Example usage
mstop_seq <- seq(5, 5000, by = 5)
result <- tuneandtrainIntBoost(sample_data_train, mstop_seq, nu = 0.1)
result$best_mstop
result$best_model
# }