Evaluate a tidylearn model

Usage

tl_evaluate(object, new_data = NULL, metrics = NULL, ...)

Arguments

object: A tidylearn model object
new_data: Optional new data for evaluation (if NULL, uses training data)
metrics: Character vector of metrics to compute. If NULL (the default), "accuracy" is used for classification models and c("rmse", "mae", "rsq") for regression models. Classification supports "accuracy", "precision", "recall", "sensitivity", "specificity", "f1", "auc" and "pr_auc"; regression supports "rmse", "mse", "mae", "mape" and "rsq".
...: Additional arguments passed to predict()

Value

A tibble with columns metric (character) and value (numeric), containing one row per requested metric.

Examples

# \donttest{
model <- tl_model(mtcars, mpg ~ wt + hp, method = "linear")
tl_evaluate(model)
#> # A tibble: 3 × 2
#>   metric value
#>   <chr>  <dbl>
#> 1 rmse   2.47 
#> 2 mae    1.90 
#> 3 rsq    0.827
tl_evaluate(model, metrics = c("rmse", "mape"))
#> # A tibble: 2 × 2
#>   metric value
#>   <chr>  <dbl>
#> 1 rmse    2.47
#> 2 mape    9.74
# }