Ensure predictors are all numeric
validate - asserts the following:
predictors must have numeric columns.
check - returns the following:
ok A logical. Does the check pass?
bad_classes A named list. The names are the names of problematic columns,
and the values are the classes of the matching column.
validate_predictors_are_numeric(predictors) check_predictors_are_numeric(predictors)
predictors |
An object to check. |
The expected way to use this validation function is to supply it the
$predictors element of the result of a call to mold().
validate_predictors_are_numeric() returns predictors invisibly.
check_predictors_are_numeric() returns a named list of two components,
ok, and bad_classes.
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.
Other validation functions:
validate_column_names(),
validate_no_formula_duplication(),
validate_outcomes_are_binary(),
validate_outcomes_are_factors(),
validate_outcomes_are_numeric(),
validate_outcomes_are_univariate(),
validate_prediction_size()
# All good check_predictors_are_numeric(mtcars) # Species is not numeric check_predictors_are_numeric(iris) # This gives an intelligent error message try(validate_predictors_are_numeric(iris))
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