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Train an EasyEnsemble Model for Imbalanced Classification

Source: R/diagnosis.R
imbalance_dia.Rd

Implements the EasyEnsemble algorithm. It trains multiple base models on balanced subsets of the data (by undersampling the majority class) and aggregates their predictions.

Usage

imbalance_dia(
  data,
  base_model_name = "xb",
  n_estimators = 10,
  tune_base_model = FALSE,
  threshold_choices = "default",
  positive_label_value = 1,
  negative_label_value = 0,
  new_positive_label = "Positive",
  new_negative_label = "Negative",
  seed = 456
)

Arguments

data

A data frame where the first column is the sample ID, the second is the outcome label, and subsequent columns are features.

base_model_name

A character string, the name of the base diagnostic model to use (e.g., "xb", "rf"). This model must be registered.

n_estimators

An integer, the number of base models to train (number of subsets).

tune_base_model

Logical, whether to enable tuning for each base 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.

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").

seed

An integer, for reproducibility.

Value

A list containing the model_object, sample_score, and evaluation_metrics.

See also

initialize_modeling_system_dia, evaluate_model_dia

Examples

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

# 2. Create an imbalanced toy dataset
set.seed(42)
n_obs <- 100
n_minority <- 10
data_imbalanced_toy <- data.frame(
  ID = paste0("Sample", 1:n_obs),
  Status = c(rep(1, n_minority), rep(0, n_obs - n_minority)),
  Feat1 = rnorm(n_obs),
  Feat2 = runif(n_obs)
)

# 3. Run the EasyEnsemble algorithm
# n_estimators is reduced for a quick example
easyensemble_results <- imbalance_dia(
  data = data_imbalanced_toy,
  base_model_name = "xb",
  n_estimators = 3,
  threshold_choices = "f1"
)
#> Running Imbalance model: EasyEnsemble_dia (base: xb)
print_model_summary_dia("EasyEnsemble (XGBoost)", easyensemble_results)
#> 
#> --- EasyEnsemble (XGBoost) Model (on Training Data) Metrics ---
#> Ensemble Type: EasyEnsemble (Base: xb, Estimators: 3)
#> 
#> AUROC: 0.9233 (95% CI: 0.8662 - 0.9805)
#> AUPRC: 0.4266
#> Accuracy: 0.9200
#> F1: 0.6364
#> Precision: 0.5833
#> Recall: 0.7000
#> Specificity: 0.9444
#> --------------------------------------------------
# }