juice: Extract transformed training set

In recipes: Preprocessing and Feature Engineering Steps for Modeling

Extract transformed training set

Description

As of recipes version 0.1.14, juice() is superseded in favor of bake(object, new_data = NULL).

As steps are estimated by prep, these operations are applied to the training set. Rather than running bake() to duplicate this processing, this function will return variables from the processed training set.

Usage

juice(object, ..., composition = "tibble")

Arguments

object	A recipe object that has been prepared with the option retain = TRUE.
...	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 use dplyr::everything().
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).

Details

juice() will return the results of a recipe where all steps have been applied to the data, irrespective of the value of the step's skip argument.

juice() can only be used if a recipe was prepped with retain = TRUE. This is equivalent to bake(object, new_data = NULL) which is the preferred way to extract the transformation of the training data set.

See Also

recipe() prep() bake()

recipes documentation built on July 4, 2024, 9:06 a.m.