table_best_hyperparameters: Best Hyperparameters Configuration

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Best Hyperparameters Configuration

Description

The table_best_hyperparameters() function extracts and presents the optimal hyperparameter configuration identified during the model fine-tuning process. This function validates that the model has been properly trained and that hyperparameter tuning has been performed, combining both constant and optimized hyperparameters to generate a comprehensive table with the configuration that maximizes performance according to the specified primary metric. The function includes optional interactive visualization capabilities through the show_table parameter.

Usage

table_best_hyperparameters(analysis_object, show_table = FALSE)

Arguments

analysis_object	Fitted analysis_object with 'fine_tuning()'.
show_table	Boolean. Whether to show the table.

Value

Tibble with best hyperparameter configuration.

Examples

# Note: For obtaining hyoperparameters table the user needs to
# complete till fine_tuning( ) function.

  

wrap_object <- preprocessing(df = sim_data,
                             formula = psych_well ~ depression + emot_intel + resilience,
                             task = "regression")
wrap_object <- build_model(wrap_object, "Random Forest")
wrap_object <- fine_tuning(wrap_object, "Bayesian Optimization")

# And then, you can obtain the best hyperparameters table.

table_best_hyp <- table_best_hyperparameters(wrap_object)

MLwrap documentation built on Aug. 8, 2025, 6:43 p.m.