bagging_dia: Train a Bagging Diagnostic Model
In E2E: Ensemble Learning Framework for Diagnostic and Prognostic Modeling

bagging_dia

R Documentation

Train a Bagging Diagnostic Model

Description

Implements a Bagging (Bootstrap Aggregating) ensemble for diagnostic models. It trains multiple base models on bootstrapped samples of the training data and aggregates their predictions by averaging probabilities.

Usage

bagging_dia(
  data,
  base_model_name,
  n_estimators = 50,
  subset_fraction = 0.632,
  tune_base_model = FALSE,
  threshold_strategy = "default",
  specific_threshold_value = 0.5,
  positive_label_value = 1,
  negative_label_value = 0,
  new_positive_label = "Positive",
  new_negative_label = "Negative",
  seed = 456
)

Arguments

`data`	A data frame where the first column is the sample ID, the second is the outcome label, and subsequent columns are features.
`base_model_name`	A character string, the name of the base diagnostic model to use (e.g., "rf", "lasso"). This model must be registered.
`n_estimators`	An integer, the number of base models to train.
`subset_fraction`	A numeric value between 0 and 1, the fraction of samples to bootstrap for each base model.
`tune_base_model`	Logical, whether to enable tuning for each base model.
`threshold_strategy`	A character string (e.g., "f1", "youden", "default") or a numeric value (0-1) for determining the evaluation threshold for the ensemble.
`specific_threshold_value`	A numeric value between 0 and 1. Only used if `threshold_strategy` is "numeric".
`positive_label_value`	A numeric or character value in the raw data representing the positive class.
`negative_label_value`	A numeric or character value in the raw data representing the negative class.
`new_positive_label`	A character string, the desired factor level name for the positive class (e.g., "Positive").
`new_negative_label`	A character string, the desired factor level name for the negative class (e.g., "Negative").
`seed`	An integer, for reproducibility.

Value

A list containing the model_object, sample_score, and evaluation_metrics.

Examples


# This example assumes your package includes a dataset named 'train_dia'.
# If not, create a toy data frame first.
if (exists("train_dia")) {
  initialize_modeling_system_dia()

  bagging_rf_results <- bagging_dia(
    data = train_dia,
    base_model_name = "rf",
    n_estimators = 5, # Reduced for a quick example
    threshold_strategy = "youden",
    positive_label_value = 1,
    negative_label_value = 0,
    new_positive_label = "Case",
    new_negative_label = "Control"
  )
  print_model_summary_dia("Bagging (RF)", bagging_rf_results)
}

E2E documentation built on Aug. 27, 2025, 1:09 a.m.