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Train a Voting Ensemble Diagnostic Model

Source: R/diagnosis.R
voting_dia.Rd

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

# \donttest{
# 1. Initialize the modeling system
initialize_modeling_system_dia()
#> Diagnostic modeling system already initialized.

# 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
)
#> Running model: rf
#> Warning: ci.auc() of a ROC curve with AUC == 1 is always 1-1 and can be misleading.
#> Running model: lasso

# 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"
)
#> Running Voting model: Voting_dia (type: soft)
#> Warning: ci.auc() of a ROC curve with AUC == 1 is always 1-1 and can be misleading.
print_model_summary_dia("Soft Voting", soft_voting_results)
#> 
#> --- Soft Voting Model (on Training Data) Metrics ---
#> Ensemble Type: Voting (Type: soft, Weight Metric: AUROC, Base models used: rf, lasso)
#> 
#> AUROC: 1.0000 (95% CI: 1.0000 - 1.0000)
#> AUPRC: 1.0000
#> Accuracy: 1.0000
#> F1: 1.0000
#> Precision: 1.0000
#> Recall: 1.0000
#> Specificity: 1.0000
#> --------------------------------------------------
# }