validation-rules.md
In tailor: Iterative Steps for Postprocessing Model Predictions

validation of adjustments (regression)

Code
  adjust_predictions_custom(adjust_numeric_calibration(adjust_numeric_range(
    tailor(), lower_limit = 2)), squared = .pred^2)
Condition
  Error in `adjust_numeric_calibration()`:
  ! Calibration should come before other adjustments.

validation of adjustments (classification)

Code
  adjust_probability_calibration(adjust_probability_threshold(tailor(),
  threshold = 0.4))
Condition
  Error in `adjust_probability_calibration()`:
  ! Adjustments that change the hard class predictions must come after adjustments that update the class probability estimates.

Code
  adjust_probability_calibration(adjust_probability_threshold(
    adjust_predictions_custom(tailor(), veg = "potato"), threshold = 0.4))
Condition
  Error in `adjust_probability_calibration()`:
  ! Adjustments that change the hard class predictions must come after adjustments that update the class probability estimates.

Code
  adjust_probability_calibration(adjust_probability_threshold(
    adjust_probability_threshold(adjust_predictions_custom(tailor(), veg = "potato"),
    threshold = 0.4), threshold = 0.5))
Condition
  Error in `adjust_probability_threshold()`:
  ! Adjustment `probability_threshold()` was duplicated.

Code
  adjust_probability_threshold(adjust_equivocal_zone(tailor(), value = 0.2),
  threshold = 0.4)
Condition
  Error in `adjust_probability_threshold()`:
  ! Equivocal zone addition should come after adjustments that update the class probability estimates or hard class predictions.

validation of adjustments (incompatible types)

Code
  adjust_probability_threshold(adjust_numeric_calibration(tailor()))
Condition
  Error in `adjust_probability_threshold()`:
  ! Can't compose adjustments for different prediction types.
  i Adjustment `adjust_numeric_calibration()` operates on numerics while `adjust_probability_threshold()` operates on probabilities.

Code
  adjust_numeric_range(adjust_probability_threshold(
    adjust_probability_calibration(tailor(), "logistic"), threshold = 0.4),
  lower_limit = 2)
Condition
  Error in `adjust_numeric_range()`:
  ! Can't compose adjustments for different prediction types.
  i Adjustment `adjust_numeric_range()` operates on numerics while `adjust_probability_calibration()` and `adjust_probability_threshold()` operate on probabilities.

Code
  adjust_probability_threshold(adjust_numeric_range(adjust_numeric_calibration(
    tailor()), lower_limit = 2), threshold = 0.4)
Condition
  Error in `adjust_probability_threshold()`:
  ! Can't compose adjustments for different prediction types.
  i Adjustments `adjust_numeric_calibration()` and `adjust_numeric_range()` operate on numerics while `adjust_probability_threshold()` operates on probabilities.

Code
  adjust_probability_threshold(adjust_numeric_calibration(
    adjust_predictions_custom(tailor(), veg = "potato")))
Condition
  Error in `adjust_probability_threshold()`:
  ! Can't compose adjustments for different prediction types.
  i Adjustment `adjust_numeric_calibration()` operates on numerics while `adjust_probability_threshold()` operates on probabilities.

Code
  adjust_numeric_range(adjust_probability_threshold(
    adjust_probability_calibration(adjust_predictions_custom(tailor(), veg = "potato"),
    "logistic"), threshold = 0.4), lower_limit = 2)
Condition
  Error in `adjust_numeric_range()`:
  ! Can't compose adjustments for different prediction types.
  i Adjustment `adjust_numeric_range()` operates on numerics while `adjust_probability_calibration()` and `adjust_probability_threshold()` operate on probabilities.

Code
  adjust_probability_threshold(adjust_numeric_range(adjust_numeric_calibration(
    adjust_predictions_custom(tailor(), veg = "potato")), lower_limit = 2),
  threshold = 0.4)
Condition
  Error in `adjust_probability_threshold()`:
  ! Can't compose adjustments for different prediction types.
  i Adjustments `adjust_numeric_calibration()` and `adjust_numeric_range()` operate on numerics while `adjust_probability_threshold()` operates on probabilities.