Executes a complete diagnostic modeling workflow including single models, bagging, stacking, and voting ensembles across training and multiple test datasets. Returns structured results with AUROC values for visualization.
int_dia(
...,
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"
)A list containing all_results, auroc_matrix, model_categories, dataset_names.
Data frames for analysis. The first is the training dataset; all subsequent arguments are test datasets.
Character vector specifying which models to use. If NULL (default), uses all registered models.
Logical, enable hyperparameter tuning. Default TRUE.
Integer, number of bootstrap samples for bagging. Default 10.
Integer for reproducibility. Default 123.
Value representing positive class. Default 1.
Value representing negative class. Default 0.
Factor level name for positive class. Default "Positive".
Factor level name for negative class. Default "Negative".