table_evaluation_results: Evaluation Results

In MLwrap: Machine Learning Modelling for Everyone

Evaluation Results

Description

The table_evaluation_results() function provides access to trained model evaluation metrics, automatically adapting to the type of problem being analyzed. For binary classification problems, it returns a unified table with performance metrics, while for multiclass classification it generates separate tables for training and test data, enabling comparative performance evaluation and detection of potential overfitting.

Usage

table_evaluation_results(analysis_object, show_table = FALSE)

Arguments

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

Value

Tibble or list of tibbles (multiclass classification) with evaluation results.

Examples

# Note: For obtaining the evaluation 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 evaluation table.

table_results <- table_evaluation_results(wrap_object)

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