Print decision tree performance according to different metrics.

Source: R/eval_tree.R

Print decision tree performance according to different metrics.

eval_tree(
  dat,
  target_lab = colnames(dat)[1],
  task = c("classification", "regression"),
  metrics = NULL
)

Arguments

dat

Dataframe with truths (column `target_lab`) and estimates (column `y_hat`) of samples from original dataset.

target_lab

Name of the column in data that contains target/label information.

task

Character string indicating the type of problem, either 'classification' (categorical outcome) or 'regression' (continuous outcome).

metrics

A set of metric functions to evaluate decision tree, defaults to common metrics for classification/regression problems. Can be defined with `yardstick::metric_set`.

Value

Character string of the decision tree evaluation.

Examples

eval_tree(compute_tree(penguins, target_lab = 'species')$dat)
#> [1] "ACCURACY: 0.973\nBAL_ACCURACY: 0.974\nKAP: 0.958\nROC_AUC: 0.822\nPR_AUC: 0.718"