Imbalanced Data Diagnostic Modeling Pipeline

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

if (FALSE) { # \dontrun{
imbalanced_results <- int_imbalance(train_imbalanced, test_imbalanced)
} # }