voting_dia: Train a Voting Ensemble Diagnostic Model

In E2E: Ensemble Learning Framework for Diagnostic and Prognostic Modeling

Train a Voting Ensemble Diagnostic Model

Description

Implements a Voting ensemble, combining predictions from multiple base models through soft or hard voting.

Usage

voting_dia(
  results_all_models,
  data,
  type = c("soft", "hard"),
  weight_metric = "AUROC",
  top = 5,
  seed = 789,
  threshold_choices = "f1",
  positive_label_value = 1,
  negative_label_value = 0,
  new_positive_label = "Positive",
  new_negative_label = "Negative"
)

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 evaluation.
type	A character string, "soft" for weighted average of probabilities or "hard" for majority class voting.
weight_metric	A character string, the metric to use for weighting base models in soft voting (e.g., "AUROC", "F1"). Ignored for hard voting.
top	An integer, the number of top-performing base models (ranked by weight_metric) to include in the ensemble.
seed	An integer, for reproducibility.
threshold_choices	A character string (e.g., "f1", "youden", "default") or a numeric value (0-1) for determining the evaluation threshold for the ensemble.
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").

Value

A list containing the model_object, sample_score, and evaluation_metrics.

See Also

models_dia, evaluate_model_dia

Examples

# 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)
base_model_results <- models_dia(
  data = data_toy,
  model = c("rf", "lasso"),
  seed = 123
)

# 4. Run the soft voting ensemble
soft_voting_results <- voting_dia(
  results_all_models = base_model_results,
  data = data_toy,
  type = "soft",
  weight_metric = "AUROC",
  top = 2,
  threshold_choices = "f1"
)
print_model_summary_dia("Soft Voting", soft_voting_results)

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