bagging_pro: Train Bagging Ensemble for Prognosis

In E2E: Ensemble Learning Framework for Diagnostic and Prognostic Modeling

Train Bagging Ensemble for Prognosis

Description

Implements Bootstrap Aggregating (Bagging) for survival models. It trains multiple base models on bootstrapped subsets and averages the risk scores. This method reduces variance and improves stability.

Usage

bagging_pro(
  data,
  base_model_name,
  n_estimators = 10,
  subset_fraction = 0.632,
  tune_base_model = FALSE,
  time_unit = "day",
  years_to_evaluate = c(1, 3, 5),
  seed = 456
)

Arguments

data	Input data frame (ID, Status, Time, Features).
base_model_name	Character string name of the base model (e.g., "rsf_pro").
n_estimators	Integer. Number of bootstrap iterations.
subset_fraction	Numeric (0-1). Fraction of data to sample in each iteration.
tune_base_model	Logical. Whether to tune each base model (computationally expensive).
time_unit	Time unit of the input data.
years_to_evaluate	Numeric vector of years for time-dependent AUC evaluation.
seed	Integer seed for reproducibility.

Value

A list containing the ensemble object, sample scores, and evaluation metrics.

E2E documentation built on March 22, 2026, 1:07 a.m.