Train model with optimized hyperparameters for classification tasks

This function wraps cross-validation, hyperparameter optimization, and final training into a single workflow. It identifies the best hyperparameters using a custom cross-validation function, reconstructs the training set, preprocesses features, and retrains the model on the complete training data with the selected hyperparameters.

Usage

wrapper_train_best_hyperparams_classification(
  train_data,
  optimized,
  ml_method,
  fold_construction_fun,
  fold_construction_args_fixed
)

Arguments

train_data

A data frame containing the full training dataset, including predictors and the target variable.

ml_method

A character string specifying the machine learning method to be passed to caret::train.

fold_construction_fun

A function used to (re)construct training data partitions given the best hyperparameters.

fold_construction_args_fixed

A named list of additional fixed arguments to pass to fold_construction_fun.

fold_data

A list or object containing pre-constructed folds for cross-validation, typically created by fold_construction_fun.

tuneGrid

(optional) A data frame of hyperparameter values to evaluate. If NULL, defaults are used.

ncores

(optional) Integer specifying the number of cores for parallel processing during cross-validation. If NULL, defaults to serial execution.

Value

A list with the following components:

Model

A trained caret model object with results, predictions, and resampling info attached.

training_set

The final preprocessed training dataset.

custom_output

Additional output from fold_construction_fun.

Details

The workflow proceeds in the following steps:

1. Runs cross-validation using compute_custom_k_fold_CV to identify the best hyperparameter set.

2. Reconstructs the training set using fold_construction_fun and the selected hyperparameters.

3. Preprocesses the training features by removing near-zero variance, highly correlated, and constant-within-class features (via preprocess_features).

4. Retrains the model on the complete training data with the optimized hyperparameters.

The returned object mimics a caret model object but includes additional elements derived from the custom cross-validation.

See also

compute_custom_k_fold_CV, preprocess_features

Examples

if (FALSE) { # \dontrun{
library(caret)

# Example placeholders
train_data <- your_training_data
fold_data  <- your_prepared_folds

result <- wrapper_train_best_hyperparams_classification(
  train_data = train_data,
  fold_data  = fold_data,
  ml_method  = "rf",
  fold_construction_fun = your_fold_fun,
  fold_construction_args_fixed = list(arg1 = "value"),
  tuneGrid = expand.grid(mtry = 2:4),
  ncores = 4
)

result$Model
} # }