E2E (version 0.1.2)

apply_dia: Apply a Trained Model to New Data

Description

Applies a trained diagnostic model (single or ensemble) to a new dataset to generate predictions. It can handle various model objects created by the package, including single caret models, Bagging, Stacking, Voting, and EasyEnsemble objects.

Usage

apply_dia(
  trained_model_object,
  new_data,
  label_col_name = NULL,
  pos_class = "Positive",
  neg_class = "Negative"
)

Value

A data frame with three columns: sample (the sample IDs), label

(the true labels from new_data, or NA if not available/specified), and score

(the predicted probability for the positive class).

Arguments

trained_model_object

A trained model object from models_dia, bagging_dia, stacking_dia, voting_dia, or imbalance_dia.

new_data

A data frame containing the new samples for prediction. The first column must be the sample ID.

label_col_name

An optional character string specifying the name of the column in new_data that contains the true labels. If NULL (the default), the function will assume the second column is the label column. To explicitly prevent label extraction (e.g., for data without labels), provide NA.

pos_class

A character string for the positive class label used in the model's probability predictions. Defaults to "Positive".

neg_class

A character string for the negative class label. This parameter is mainly for consistency, as prediction focuses on pos_class probability. Defaults to "Negative".

Examples

# \donttest{
# Assuming `bagging_results` and `test_dia` are available from previous steps
# bagging_model <- bagging_results$model_object

# Example 1: Default behavior - use the second column of test_dia as label
# predictions <- apply_dia(
#   trained_model_object = bagging_model,
#   new_data = test_dia
# )

# Example 2: Explicitly specify the label column by name
# predictions_explicit <- apply_dia(
#   trained_model_object = bagging_model,
#   new_data = test_dia,
#   label_col_name = "outcome"
# )

# Example 3: Predict on data without labels
# test_data_no_labels <- test_dia[, -2] # Remove outcome column
# predictions_no_label <- apply_dia(
#   trained_model_object = bagging_model,
#   new_data = test_data_no_labels,
#   label_col_name = NA # Explicitly disable label extraction
# )
# }