MLwrap (version 0.1.0)

plot_confusion_matrix: Plotting Confusion Matrix

Description

The plot_confusion_matrix() function generates confusion matrices for both training and test data in classification problems. This visualization allows evaluation of classification accuracy by category and identification of confusion patterns between classes, providing insights into which classes are most frequently misclassified.

Usage

plot_confusion_matrix(analysis_object)

Value

analysis_object

Arguments

analysis_object

Fitted analysis_object with 'fine_tuning()'.

Examples

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

# \donttest{

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 confusion matrix plot.

plot_confusion_matrix(wrap_object)

# }