aaa-get_metrics.md
In yardstick: Tidy Characterizations of Model Performance

get_metrics() returns metric_set

Code
  res
Output
  A metric set, consisting of:
  - `ccc()`, a numeric metric               | direction: maximize
  - `gini_coef()`, a numeric metric         | direction: maximize
  - `huber_loss()`, a numeric metric        | direction: minimize
  - `huber_loss_pseudo()`, a numeric metric | direction: minimize
  - `iic()`, a numeric metric               | direction: maximize
  - `mae()`, a numeric metric               | direction: minimize
  - `mape()`, a numeric metric              | direction: minimize
  - `mase()`, a numeric metric              | direction: minimize
  - `mpe()`, a numeric metric               | direction: zero
  - `msd()`, a numeric metric               | direction: zero
  - `mse()`, a numeric metric               | direction: minimize
  - `poisson_log_loss()`, a numeric metric  | direction: minimize
  - `rmse()`, a numeric metric              | direction: minimize
  - `rmse_relative()`, a numeric metric     | direction: minimize
  - `rpd()`, a numeric metric               | direction: maximize
  - `rpiq()`, a numeric metric              | direction: maximize
  - `rsq()`, a numeric metric               | direction: maximize
  - `rsq_trad()`, a numeric metric          | direction: maximize
  - `smape()`, a numeric metric             | direction: minimize

get_metrics() errors on empty input

Code
  get_metrics()
Condition
  Error in `get_metrics()`:
  ! `type` must be a character vector, not absent.

get_metrics() errors on invalid type

Code
  get_metrics("invalid")
Condition
  Error in `get_metrics()`:
  ! `type` must be one of "class", "dynamic_survival", "groupwise", "integrated_survival", "linear_pred_survival", "numeric", "ordered_prob", "prob", "quantile", or "static_survival", not "invalid".

get_metrics() errors on incompatible metric types

Code
  get_metrics(c("class", "numeric"))
Condition
  Error in `metric_set()`:
  x The combination of metric functions must be:
  * only numeric metrics.
  * a mix of class metrics and class probability metrics.
  * a mix of dynamic and static survival metrics.
  i The following metric function types are being mixed:
  - class (accuracy, bal_accuracy, detection_prevalence, f_meas, fall_out, j_index, kap, markedness, mcc, miss_rate, npv, ppv, precision, recall, roc_dist, sedi, sens, sensitivity, spec, specificity)
  - numeric (ccc, gini_coef, huber_loss, huber_loss_pseudo, iic, mae, mape, mase, mpe, msd, mse, poisson_log_loss, rmse, rmse_relative, rpd, rpiq, rsq, rsq_trad, smape)