int_imbalance: Imbalanced Data Diagnostic Modeling Pipeline

In E2E: Ensemble Learning Framework for Diagnostic and Prognostic Modeling

Imbalanced Data Diagnostic Modeling Pipeline

Description

Extends int_dia by adding imbalance-specific models (EasyEnsemble). Produces a comprehensive set of models optimized for imbalanced datasets.

Usage

int_imbalance(
  ...,
  model_names = NULL,
  tune = TRUE,
  n_estimators = 10,
  seed = 123,
  positive_label_value = 1,
  negative_label_value = 0,
  new_positive_label = "Positive",
  new_negative_label = "Negative"
)

Arguments

...	Data frames for analysis. The first is the training dataset; all subsequent arguments are test datasets.
model_names	Character vector specifying which models to use. If NULL (default), uses all registered models.
tune	Logical, enable hyperparameter tuning. Default TRUE.
n_estimators	Integer, number of bootstrap samples for bagging. Default 10.
seed	Integer for reproducibility. Default 123.
positive_label_value	Value representing positive class. Default 1.
negative_label_value	Value representing negative class. Default 0.
new_positive_label	Factor level name for positive class. Default "Positive".
new_negative_label	Factor level name for negative class. Default "Negative".

Value

Same structure as int_dia with additional imbalance-handling models.

Examples

## Not run: 
imbalanced_results <- int_imbalance(train_imbalanced, test_imbalanced)

## End(Not run)

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