RobustPrediction (version 0.1.7)

tuneandtrainRobustTuneCBoost: Tune and Train RobustTuneC Boosting

Description

This function tunes and trains a Boosting classifier using the mboost::glmboost function and the "RobustTuneC" method. The function performs K-fold cross-validation on the training dataset and evaluates a sequence of boosting iterations (mstop) based on the Area Under the Curve (AUC).

Usage

tuneandtrainRobustTuneCBoost(
  data,
  dataext,
  K = 5,
  mstop_seq = seq(5, 1000, by = 5),
  nu = 0.1
)

Value

A list containing the best number of boosting iterations (`best_mstop`), the final trained model (`best_model`), and the chosen c value(`best_c`).

Arguments

data

Training data as a data frame. The first column should be the response variable.

dataext

External validation data as a data frame. The first column should be the response variable.

K

Number of folds to use in cross-validation. Default is 5.

mstop_seq

A sequence of boosting iterations to consider. Default is a sequence starting at 5 and increasing by 5 each time, up to 1000.

nu

Learning rate for the boosting algorithm. Default is 0.1.

Details

After cross-validation, the best mstop value is selected based on the AUC, and the final Boosting model is trained using this optimal mstop. The external validation dataset is then used to calculate the final AUC and assess the model performance.

Examples

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

# Example usage with the sample data
mstop_seq <- seq(50, 500, by = 50)
result <- tuneandtrainRobustTuneCBoost(sample_data_train, sample_data_extern, mstop_seq = mstop_seq)
result$best_mstop
result$best_model
result$best_c