For a recipe with at least one preprocessing operation that has been trained
by prep(), apply the computations to new data.
Usage
bake(object, ...)
# S3 method for class 'recipe'
bake(object, new_data, ..., composition = "tibble")Arguments
- object
A trained object such as a
recipe()with at least one preprocessing operation.- ...
One or more selector functions to choose which variables will be returned by the function. See
selections()for more details. If no selectors are given, the default is to usedplyr::everything().- new_data
A data frame, tibble, or sparse matrix from the
Matrixpackage for whom the preprocessing will be applied. IfNULLis given tonew_data, the pre-processed training data will be returned (assuming thatprep(retain = TRUE)was used). See sparse_data for more information about use of sparse data.- composition
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).
Value
A tibble, matrix, or sparse matrix that may have different columns than the
original columns in new_data.
Details
bake() takes a trained recipe and applies its operations to a data set to
create a design matrix. If you are using a recipe as a preprocessor for
modeling, we highly recommend that you use a workflow() instead of
manually applying a recipe (see the example in recipe()).
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 data when
bake() is invoked with a data set in new_data. bake(object, new_data = NULL) will always have all of the steps applied.
Examples
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)
bake(ames_rec, new_data = NULL)
#> # A tibble: 2,924 × 259
#> Lot_Frontage Lot_Area Year_Built Year_Remod_Add Mas_Vnr_Area
#> <dbl> <int> <int> <int> <dbl>
#> 1 41 4920 2001 2001 0
#> 2 43 5005 1992 1992 0
#> 3 39 5389 1995 1996 0
#> 4 60 7500 1999 1999 0
#> 5 75 10000 1993 1994 0
#> 6 0 7980 1992 2007 0
#> 7 63 8402 1998 1998 0
#> 8 85 10176 1990 1990 0
#> 9 0 6820 1985 1985 0
#> 10 47 53504 2003 2003 603
#> # ℹ 2,914 more rows
#> # ℹ 254 more variables: BsmtFin_SF_1 <dbl>, BsmtFin_SF_2 <dbl>,
#> # Bsmt_Unf_SF <dbl>, Total_Bsmt_SF <dbl>, First_Flr_SF <int>,
#> # Second_Flr_SF <int>, Gr_Liv_Area <int>, Bsmt_Full_Bath <dbl>,
#> # Bsmt_Half_Bath <dbl>, Full_Bath <int>, Half_Bath <int>,
#> # Bedroom_AbvGr <int>, Kitchen_AbvGr <int>, TotRms_AbvGrd <int>,
#> # Fireplaces <int>, Garage_Cars <dbl>, Garage_Area <dbl>, …
# apply processing to other data:
bake(ames_rec, new_data = head(ames))
#> # A tibble: 6 × 259
#> Lot_Frontage Lot_Area Year_Built Year_Remod_Add Mas_Vnr_Area
#> <dbl> <int> <int> <int> <dbl>
#> 1 141 31770 1960 1960 112
#> 2 80 11622 1961 1961 0
#> 3 81 14267 1958 1958 108
#> 4 93 11160 1968 1968 0
#> 5 74 13830 1997 1998 0
#> 6 78 9978 1998 1998 20
#> # ℹ 254 more variables: BsmtFin_SF_1 <dbl>, BsmtFin_SF_2 <dbl>,
#> # Bsmt_Unf_SF <dbl>, Total_Bsmt_SF <dbl>, First_Flr_SF <int>,
#> # Second_Flr_SF <int>, Gr_Liv_Area <int>, Bsmt_Full_Bath <dbl>,
#> # Bsmt_Half_Bath <dbl>, Full_Bath <int>, Half_Bath <int>,
#> # Bedroom_AbvGr <int>, Kitchen_AbvGr <int>, TotRms_AbvGrd <int>,
#> # Fireplaces <int>, Garage_Cars <dbl>, Garage_Area <dbl>,
#> # Wood_Deck_SF <int>, Open_Porch_SF <int>, Enclosed_Porch <int>, …
# only return selected variables:
bake(ames_rec, new_data = head(ames), all_numeric_predictors())
#> # A tibble: 6 × 258
#> Lot_Frontage Lot_Area Year_Built Year_Remod_Add Mas_Vnr_Area
#> <dbl> <int> <int> <int> <dbl>
#> 1 141 31770 1960 1960 112
#> 2 80 11622 1961 1961 0
#> 3 81 14267 1958 1958 108
#> 4 93 11160 1968 1968 0
#> 5 74 13830 1997 1998 0
#> 6 78 9978 1998 1998 20
#> # ℹ 253 more variables: BsmtFin_SF_1 <dbl>, BsmtFin_SF_2 <dbl>,
#> # Bsmt_Unf_SF <dbl>, Total_Bsmt_SF <dbl>, First_Flr_SF <int>,
#> # Second_Flr_SF <int>, Gr_Liv_Area <int>, Bsmt_Full_Bath <dbl>,
#> # Bsmt_Half_Bath <dbl>, Full_Bath <int>, Half_Bath <int>,
#> # Bedroom_AbvGr <int>, Kitchen_AbvGr <int>, TotRms_AbvGrd <int>,
#> # Fireplaces <int>, Garage_Cars <dbl>, Garage_Area <dbl>,
#> # Wood_Deck_SF <int>, Open_Porch_SF <int>, Enclosed_Porch <int>, …
bake(ames_rec, new_data = head(ames), starts_with(c("Longitude", "Latitude")))
#> # A tibble: 6 × 4
#> Longitude_ns_1 Longitude_ns_2 Latitude_ns_1 Latitude_ns_2
#> <dbl> <dbl> <dbl> <dbl>
#> 1 0.570 -0.0141 0.472 0.394
#> 2 0.570 -0.0142 0.481 0.360
#> 3 0.569 -0.00893 0.484 0.348
#> 4 0.563 0.0212 0.496 0.301
#> 5 0.562 -0.212 0.405 0.634
#> 6 0.562 -0.212 0.407 0.630