Calculate classification metrics

Usage

tl_calc_classification_metrics(
  actuals,
  predicted,
  predicted_probs = NULL,
  metrics = c("accuracy", "precision", "recall", "f1", "auc"),
  thresholds = NULL,
  ...
)

Arguments

actuals: Actual values (ground truth)
predicted: Predicted class values
predicted_probs: Predicted probabilities (for metrics like AUC)
metrics: Character vector of metrics to compute
thresholds: Optional vector of thresholds to evaluate for threshold-dependent metrics
...: Additional arguments

Value

A tibble with columns metric (character) and value (numeric) containing the requested classification metrics. When thresholds are supplied, additional rows are appended with threshold-specific metric names.

Examples

# \donttest{
model <- tl_model(iris, Species ~ ., method = "forest")
preds <- predict(model)
tl_calc_classification_metrics(iris$Species, preds$.pred)
#> # A tibble: 4 × 2
#>   metric    value
#>   <chr>     <dbl>
#> 1 accuracy      1
#> 2 precision     1
#> 3 recall        1
#> 4 f1            1
# }