validate - asserts the following:

  • The size of pred must be the same as the size of new_data.

check - returns the following:

  • ok A logical. Does the check pass?

  • size_new_data A single numeric. The size of new_data.

  • size_pred A single numeric. The size of pred.

validate_prediction_size(pred, new_data)

check_prediction_size(pred, new_data)



A tibble. The predictions to return from any prediction type. This is often created using one of the spruce functions, like spruce_numeric().


A data frame of new predictors and possibly outcomes.


validate_prediction_size() returns pred invisibly.

check_prediction_size() returns a named list of three components, ok, size_new_data, and size_pred.


This validation function is one that is more developer focused rather than user focused. It is a final check to be used right before a value is returned from your specific predict() method, and is mainly a "good practice" sanity check to ensure that your prediction blueprint always returns the same number of rows as new_data, which is one of the modeling conventions this package tries to promote.


hardhat provides validation functions at two levels.

  • check_*(): check a condition, and return a list. The list always contains at least one element, ok, a logical that specifies if the check passed. Each check also has check specific elements in the returned list that can be used to construct meaningful error messages.

  • validate_*(): check a condition, and error if it does not pass. These functions call their corresponding check function, and then provide a default error message. If you, as a developer, want a different error message, then call the check_*() function yourself, and provide your own validation function.

See also


# Say new_data has 5 rows new_data <- mtcars[1:5,] # And somehow you generate predictions # for those 5 rows pred_vec <- 1:5 # Then you use `spruce_numeric()` to clean # up these numeric predictions pred <- spruce_numeric(pred_vec) pred
#> # A tibble: 5 x 1 #> .pred #> <int> #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5
# Use this check to ensure that # the number of rows or pred match new_data check_prediction_size(pred, new_data)
#> $ok #> [1] TRUE #> #> $size_new_data #> [1] 5 #> #> $size_pred #> [1] 5 #>
# An informative error message is thrown # if the rows are different try(validate_prediction_size(spruce_numeric(1:4), new_data))
#> Error : The size of `new_data` (5) must match the size of `pred` (4).