Forge prediction-ready data
forge() applies the transformations requested by the specific blueprint
on a set of new_data. This new_data contains new predictors
(and potentially outcomes) that will be used to generate predictions.
All blueprints have consistent return values with the others, but each is
unique enough to have its own help page. Click through below to learn
how to use each one in conjunction with forge().
XY Method - default_xy_blueprint()
Formula Method - default_formula_blueprint()
Recipes Method - default_recipe_blueprint()
forge(new_data, blueprint, ..., outcomes = FALSE)
new_data |
A data frame or matrix of predictors to process. If
|
blueprint |
A preprocessing |
... |
Not used. |
outcomes |
A logical. Should the outcomes be processed and returned as well? |
If the outcomes are present in new_data, they can optionally be processed
and returned in the outcomes slot of the returned list by setting
outcomes = TRUE. This is very useful when doing cross validation where
you need to preprocess the outcomes of a test set before computing
performance.
A named list with 3 elements:
predictors: A tibble containing the preprocessed
new_data predictors.
outcomes: If outcomes = TRUE, a tibble containing the preprocessed
outcomes found in new_data. Otherwise, NULL.
extras: Either NULL if the blueprint returns no extra information,
or a named list containing the extra information.
# See the blueprint specific documentation linked above # for various ways to call forge with different # blueprints. train <- iris[1:100,] test <- iris[101:150,] # Formula processed <- mold( log(Sepal.Width) ~ Species, train, blueprint = default_formula_blueprint(indicators = "none") ) forge(test, processed$blueprint, outcomes = TRUE)
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