For a recipe with at least one preprocessing operation that has been trained by
prep.recipe(), apply the computations to new data.
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A trained object such as a
One or more selector functions to choose which variables will be
returned by the function. See
A data frame or tibble for whom the preprocessing will be
Either "tibble", "matrix", "data.frame", or "dgCMatrix" for the format of the processed data set. Note that all computations during the baking process are done in a non-sparse format. Also, note that this argument should be called after any selectors and the selectors should only resolve to numeric columns (otherwise an error is thrown).
bake() takes a trained recipe and applies the
operations to a data set to create a design matrix.
If the data set is not too large, time can be saved by using the
retain = TRUE option of
prep(). This stores the processed version of the
training set. With this option set,
bake(object, new_data = NULL)
will return it for free.
Also, any steps with
skip = TRUE will not be applied to the
bake() is invoked with a data set in
bake(object, new_data = NULL) will always have all of the steps applied.
A tibble, matrix, or sparse matrix that may have different
columns than the original columns in
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data(ames, package = "modeldata") ames <- mutate(ames, Sale_Price = log10(Sale_Price)) ames_rec <- recipe(Sale_Price ~ ., data = ames[-(1:6), ]) %>% step_other(Neighborhood, threshold = 0.05) %>% step_dummy(all_nominal()) %>% step_interact(~ starts_with("Central_Air"):Year_Built) %>% step_ns(Longitude, Latitude, deg_free = 2) %>% step_zv(all_predictors()) %>% prep() # return the training set (already embedded in ames_rec) ames_train <- bake(ames_rec, new_data = NULL) # apply processing to other data: ames_new <- bake(ames_rec, new_data = head(ames))
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