tuneandtrainIntBoost {RobustPrediction} | R Documentation |
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)
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.
Value
A list containing the best number of boosting iterations ('best_mstop') and the final Boosting classifier model ('best_model').
Examples
# 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