plot_roc_curve: Plotting ROC Curve

In MLwrap: Machine Learning Modelling for Everyone

Plotting ROC Curve

Description

The plot_roc_curve() function produces ROC (Receiver Operating Characteristic) curves, providing fundamental visual metrics for evaluating binary and multiclass classifier performance. The ROC curve illustrates the trade-off between true positive rate and false positive rate across different classification thresholds.

Usage

plot_roc_curve(analysis_object)

Arguments

analysis_object

Fitted analysis_object with 'fine_tuning()'.

Value

analysis_object

Examples

# Note: For obtaining roc curve plot the user needs to
# complete till fine_tuning( ) function of the MLwrap pipeline and
# only with categorical outcome.



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

# And then, you can obtain the roc curve plot.

plot_roc_curve(wrap_object)

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