table_pfi_results: Permutation Feature Importance Results Table

In MLwrap: Machine Learning Modelling for Everyone

Permutation Feature Importance Results Table

Description

The table_pfi_results() function extracts Permutation Feature Importance results, a model-agnostic technique that evaluates variable importance through performance degradation when randomly permuting each feature's values.

Usage

table_pfi_results(analysis_object, show_table = FALSE)

Arguments

analysis_object	Fitted analysis_object with 'sensitivity_analysis(methods = "PFI")'.
show_table	Boolean. Whether to show the table.

Value

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

Examples

# Note: For obtaining the table with PFI method results the user needs to
# complete till sensitivity_analysis() function of the
# MLwrap pipeline using PFI method.



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")
wrap_object <- sensitivity_analysis(wrap_object, methods = "PFI")

# And then, you can obtain the PFI results table.

table_pfi <- table_pfi_results(wrap_object)

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