utils.md
In tailor: Iterative Steps for Postprocessing Model Predictions

check_tailor raises informative error

Code
  adjust_probability_threshold("boop")
Condition
  Error in `adjust_probability_threshold()`:
  ! `x` should be a <tailor> (`?tailor::tailor()`), not a string.

check_calibration_type errors informatively

Code
  check_calibration_type("probability", "numeric", "regression")
Condition
  Error in `check_calibration_type()`:
  ! A regression tailor is incompatible with the adjustment `adjust_probability_calibration()`.

Code
  check_calibration_type("numeric", "probability", "binary")
Condition
  Error in `check_calibration_type()`:
  ! A binary tailor is incompatible with the adjustment `adjust_numeric_calibration()`.

Code
  check_calibration_type("numeric", "probability", "multiclass")
Condition
  Error in `check_calibration_type()`:
  ! A multiclass tailor is incompatible with the adjustment `adjust_numeric_calibration()`.

errors informatively without probably installed

Code
  adjust_numeric_calibration(tailor())
Condition
  Error in `adjust_numeric_calibration()`:
  ! The probably package must be available to use this adjustment.

fit.tailor() errors informatively with incompatible outcome

Code
  fit(adjust_probability_threshold(tailor(), 0.1), two_class_example, outcome = c(
    test_numeric), estimate = c(predicted), probabilities = c(Class1, Class2))
Condition
  Error in `fit()`:
  ! Tailors with binary adjustments are not compatible with a <numeric> `outcome`.

Code
  fit(adjust_numeric_range(tailor(), lower_limit = 0.1), two_class_example,
  outcome = c(truth), estimate = c(Class1))
Condition
  Error in `fit()`:
  ! Tailors with regression adjustments are not compatible with a <factor> `outcome`.

Code
  fit(adjust_probability_threshold(tailor(), 0.1), two_class_example, outcome = c(
    test_date), estimate = c(predicted), probabilities = c(Class1, Class2))
Condition
  Error in `fit()`:
  ! Tailors with binary adjustments are not compatible with a <POSIXct/POSIXt> `outcome`.

Code
  fit(adjust_predictions_custom(tailor(), hey = "there"), two_class_example,
  outcome = c(test_date), estimate = c(predicted), probabilities = c(Class1))
Condition
  Error in `fit()`:
  ! Only factor and numeric outcomes are currently supported.

fit.tailor() errors informatively with incompatible estimate

Code
  fit(adjust_probability_threshold(tailor(), 0.1), two_class_example, outcome = c(
    predicted), estimate = c(test_numeric), probabilities = c(Class1, Class2))
Condition
  Error in `fit()`:
  ! Tailors with binary adjustments are not compatible with a <numeric> `estimate`.

Code
  fit(adjust_numeric_range(tailor(), lower_limit = 0.1), two_class_example,
  outcome = c(Class1), estimate = c(truth))
Condition
  Error in `fit()`:
  ! Tailors with regression adjustments are not compatible with a <factor> `estimate`.

Code
  fit(adjust_probability_threshold(tailor(), 0.1), two_class_example, outcome = c(
    truth), estimate = c(test_date), probabilities = c(Class1, Class2))
Condition
  Error in `fit()`:
  ! Tailors with binary adjustments are not compatible with a <POSIXct/POSIXt> `estimate`.

Code
  fit(adjust_predictions_custom(tailor(), hey = "there"), two_class_example,
  outcome = c(truth), estimate = c(test_date), probabilities = c(Class1))
Condition
  Error in `fit()`:
  ! Tailors with binary adjustments are not compatible with a <POSIXct/POSIXt> `estimate`.

fit.tailor() errors informatively with incompatible probability

Code
  fit(adjust_probability_threshold(tailor(), 0.1), two_class_example, outcome = c(
    truth), estimate = c(predicted), probabilities = c(test_date))
Condition
  Error in `fit()`:
  ! Tailors with probability adjustments are not compatible with a <POSIXct/POSIXt> `probabilities`.

find_tune_id() works

Code
  find_tune_id(x)
Condition
  Error:
  ! Only one tunable value is currently allowed per argument.
  `x` has `list(a = tune(), b = tune())`.

check_selection() errors informatively

Code
  check_selection(quote(contains("boop")), numeric(0), ".data")
Condition
  Error:
  ! `.data` must select at least one column.
  x Selector `contains("boop")` did not match any columns in `.data`.