E2E (version 0.1.2)

stacking_dia: Train a Stacking Diagnostic Model

Description

Implements a Stacking ensemble. It trains multiple base models, then uses their predictions as features to train a meta-model.

Usage

stacking_dia(
  results_all_models,
  data,
  meta_model_name,
  top = 5,
  tune_meta = FALSE,
  threshold_choices = "f1",
  seed = 789,
  positive_label_value = 1,
  negative_label_value = 0,
  new_positive_label = "Positive",
  new_negative_label = "Negative"
)

Value

A list containing the model_object, sample_score, and evaluation_metrics.

Arguments

results_all_models

A list of results from models_dia(), containing trained base model objects and their evaluation metrics.

data

A data frame where the first column is the sample ID, the second is the outcome label, and subsequent columns are features. Used for training the meta-model.

meta_model_name

A character string, the name of the meta-model to use (e.g., "lasso", "gbm"). This model must be registered.

top

An integer, the number of top-performing base models (ranked by AUROC) to select for the stacking ensemble.

tune_meta

Logical, whether to enable tuning for the meta-model.

threshold_choices

A character string (e.g., "f1", "youden", "default") or a numeric value (0-1) for determining the evaluation threshold for the ensemble.

seed

An integer, for reproducibility.

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").

See Also

models_dia, evaluate_model_dia

Examples

# \donttest{
# 1. Initialize the modeling system
initialize_modeling_system_dia()

# 2. Create a toy dataset for demonstration
set.seed(42)
data_toy <- data.frame(
  ID = paste0("Sample", 1:60),
  Status = sample(c(0, 1), 60, replace = TRUE),
  Feat1 = rnorm(60),
  Feat2 = runif(60)
)

# 3. Generate mock base model results (as if from models_dia)
# In a real scenario, you would run models_dia() on your full dataset
base_model_results <- models_dia(
  data = data_toy,
  model = c("rf", "lasso"),
  seed = 123
)

# 4. Run the stacking ensemble
stacking_results <- stacking_dia(
  results_all_models = base_model_results,
  data = data_toy,
  meta_model_name = "gbm",
  top = 2,
  threshold_choices = "f1"
)
print_model_summary_dia("Stacking (GBM)", stacking_results)
# }