Print decision tree performance according to different metrics.
Dataframe with truths (column `target_lab`) and estimates (column `y_hat`) of samples from original dataset.
Name of the column in data that contains target/label information.
Character string indicating the type of problem, either 'classification' (categorical outcome) or 'regression' (continuous outcome).
A set of metric functions to evaluate decision tree, defaults to common metrics for classification/regression problems. Can be defined with `yardstick::metric_set`.
Character string of the decision tree evaluation.
eval_tree(compute_tree(penguins, target_lab = 'species')$dat) #>  "ACCURACY: 0.973\nBAL_ACCURACY: 0.974\nKAP: 0.958\nROC_AUC: 0.831\nPR_AUC: 0.725"