metric-args.md
In tune: Tidy Tuning Tools

metric inputs are checked for regression models

Code
  check_metrics_arg(NULL, wflow)
Output
  A metric set, consisting of:
  - `rmse()`, a numeric metric | direction: minimize
  - `rsq()`, a numeric metric  | direction: maximize

Code
  check_metrics_arg(met_reg, wflow)
Output
  A metric set, consisting of:
  - `rmse()`, a numeric metric | direction: minimize

Code
  check_metrics_arg(met_cls, wflow)
Condition
  Error:
  ! The parsnip model has `mode` value of "regression", but the `metrics` is a metric set for a different model mode.

Code
  check_metrics_arg(met_mix_int, wflow)
Condition
  Error:
  ! The parsnip model has `mode` value of "regression", but the `metrics` is a metric set for a different model mode.

Code
  fit_resamples(wflow, rs, metrics = met_cls)
Condition
  Error in `fit_resamples()`:
  ! The parsnip model has `mode` value of "regression", but the `metrics` is a metric set for a different model mode.

Code
  fit_resamples(wflow, rs, metrics = met_mix_int)
Condition
  Error in `fit_resamples()`:
  ! The parsnip model has `mode` value of "regression", but the `metrics` is a metric set for a different model mode.

Code
  tune_grid(wflow_tune, rs, metrics = met_cls)
Condition
  Error in `tune_grid()`:
  ! The parsnip model has `mode` value of "regression", but the `metrics` is a metric set for a different model mode.

Code
  tune_grid(wflow_tune, rs, metrics = met_mix_int)
Condition
  Error in `tune_grid()`:
  ! The parsnip model has `mode` value of "regression", but the `metrics` is a metric set for a different model mode.

Code
  tune_bayes(wflow_tune, rs, metrics = met_cls)
Condition
  Error in `tune_bayes()`:
  ! The parsnip model has `mode` value of "regression", but the `metrics` is a metric set for a different model mode.

Code
  tune_bayes(wflow_tune, rs, metrics = met_mix_int)
Condition
  Error in `tune_bayes()`:
  ! The parsnip model has `mode` value of "regression", but the `metrics` is a metric set for a different model mode.

Code
  last_fit(wflow, split, metrics = met_cls)
Condition
  Error in `last_fit()`:
  ! The parsnip model has `mode` value of "regression", but the `metrics` is a metric set for a different model mode.

Code
  last_fit(wflow, split, metrics = met_mix_int)
Condition
  Error in `last_fit()`:
  ! The parsnip model has `mode` value of "regression", but the `metrics` is a metric set for a different model mode.

metric inputs are checked for classification models

Code
  check_metrics_arg(NULL, wflow)
Output
  A metric set, consisting of:
  - `roc_auc()`, a probability metric     | direction: maximize
  - `accuracy()`, a class metric          | direction: maximize
  - `brier_class()`, a probability metric | direction: minimize

Code
  check_metrics_arg(met_reg, wflow)
Condition
  Error:
  ! The parsnip model has `mode` value of "classification", but the `metrics` is a metric set for a different model mode.

Code
  check_metrics_arg(met_cls, wflow)
Output
  A metric set, consisting of:
  - `brier_class()`, a probability metric | direction: minimize

Code
  check_metrics_arg(met_mix_int, wflow)
Condition
  Error:
  ! The parsnip model has `mode` value of "classification", but the `metrics` is a metric set for a different model mode.

Code
  fit_resamples(wflow, rs, metrics = met_reg)
Condition
  Error in `fit_resamples()`:
  ! The parsnip model has `mode` value of "classification", but the `metrics` is a metric set for a different model mode.

Code
  fit_resamples(wflow, rs, metrics = met_mix_int)
Condition
  Error in `fit_resamples()`:
  ! The parsnip model has `mode` value of "classification", but the `metrics` is a metric set for a different model mode.

Code
  tune_grid(wflow_tune, rs, metrics = met_reg)
Condition
  Error in `tune_grid()`:
  ! The parsnip model has `mode` value of "classification", but the `metrics` is a metric set for a different model mode.

Code
  tune_grid(wflow_tune, rs, metrics = met_mix_int)
Condition
  Error in `tune_grid()`:
  ! The parsnip model has `mode` value of "classification", but the `metrics` is a metric set for a different model mode.

Code
  tune_bayes(wflow_tune, rs, metrics = met_reg)
Condition
  Error in `tune_bayes()`:
  ! The parsnip model has `mode` value of "classification", but the `metrics` is a metric set for a different model mode.

Code
  tune_bayes(wflow_tune, rs, metrics = met_mix_int)
Condition
  Error in `tune_bayes()`:
  ! The parsnip model has `mode` value of "classification", but the `metrics` is a metric set for a different model mode.

Code
  last_fit(wflow, split, metrics = met_reg)
Condition
  Error in `last_fit()`:
  ! The parsnip model has `mode` value of "classification", but the `metrics` is a metric set for a different model mode.

Code
  last_fit(wflow, split, metrics = met_mix_int)
Condition
  Error in `last_fit()`:
  ! The parsnip model has `mode` value of "classification", but the `metrics` is a metric set for a different model mode.

metric inputs are checked for censored regression models

Code
  check_metrics_arg(NULL, wflow)
Output
  A metric set, consisting of:
  - `brier_survival()`, a dynamic survival metric | direction: minimize

Code
  check_metrics_arg(met_reg, wflow)
Condition
  Error:
  ! The parsnip model has `mode` value of "censored regression", but the `metrics` is a metric set for a different model mode.

Code
  check_metrics_arg(met_cls, wflow)
Condition
  Error:
  ! The parsnip model has `mode` value of "censored regression", but the `metrics` is a metric set for a different model mode.

Code
  check_metrics_arg(met_srv, wflow)
Output
  A metric set, consisting of:
  - `concordance_survival()`, a static survival metric | direction: maximize

Code
  fit_resamples(wflow, rs, metrics = met_cls)
Condition
  Error in `fit_resamples()`:
  ! The parsnip model has `mode` value of "censored regression", but the `metrics` is a metric set for a different model mode.

Code
  fit_resamples(wflow, rs, metrics = met_reg)
Condition
  Error in `fit_resamples()`:
  ! The parsnip model has `mode` value of "censored regression", but the `metrics` is a metric set for a different model mode.

Code
  tune_grid(wflow_tune, rs, metrics = met_cls)
Condition
  Error in `tune_grid()`:
  ! The parsnip model has `mode` value of "censored regression", but the `metrics` is a metric set for a different model mode.

Code
  tune_grid(wflow_tune, rs, metrics = met_reg)
Condition
  Error in `tune_grid()`:
  ! The parsnip model has `mode` value of "censored regression", but the `metrics` is a metric set for a different model mode.

Code
  tune_bayes(wflow_tune, rs, metrics = met_cls)
Condition
  Error in `tune_bayes()`:
  ! The parsnip model has `mode` value of "censored regression", but the `metrics` is a metric set for a different model mode.

Code
  tune_bayes(wflow_tune, rs, metrics = met_reg)
Condition
  Error in `tune_bayes()`:
  ! The parsnip model has `mode` value of "censored regression", but the `metrics` is a metric set for a different model mode.

Code
  last_fit(wflow, split, metrics = met_cls)
Condition
  Error in `last_fit()`:
  ! The parsnip model has `mode` value of "censored regression", but the `metrics` is a metric set for a different model mode.

Code
  last_fit(wflow, split, metrics = met_reg)
Condition
  Error in `last_fit()`:
  ! The parsnip model has `mode` value of "censored regression", but the `metrics` is a metric set for a different model mode.