Up-Sample a Data Set Based on a Factor VariableSource:
step_upsample is now available as
function creates a specification of a recipe step that
will replicate rows of a data set to make the occurrence of
levels in a specific factor level equal.
A recipe object. The step will be added to the sequence of operations for this recipe.
One or more selector functions to choose variables for this step. See
selections()for more details.
A numeric value for the ratio of the majority-to-minority frequencies. The default value (1) means that all other levels are sampled up to have the same frequency as the most occurring level. A value of 0.5 would mean that the minority levels will have (at most) (approximately) half as many rows than the majority level.
Deprecated argument; same as
Not used by this step since no new variables are created.
A logical to indicate if the quantities for preprocessing have been estimated.
A character string of the variable name that will be populated (eventually) by the
An integer that will be used to subsample. This should not be set by the user and will be populated by
A logical. Should the step be skipped when the recipe is baked by
bake()? While all operations are baked when
prep()is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using
skip = TRUEas it may affect the computations for subsequent operations.
An integer that will be used as the seed when upsampling.
A character string that is unique to this step to identify it.
An updated version of
recipe with the new step added to the
sequence of any existing operations.
Up-sampling is intended to be performed on the training set alone. For
this reason, the default is
skip = TRUE. It is advisable to use
prep(recipe, retain = TRUE) when preparing the recipe; in this way
bake(object, new_data = NULL) can be used to obtain the up-sampled version
of the data.
If there are missing values in the factor variable that is used to define the sampling, missing data are selected at random in the same way that the other factor levels are sampled. Missing values are not used to determine the amount of data in the majority level (see example below).
For any data with factor levels occurring with the same frequency as the majority level, all data will be retained.
All columns in the data are sampled and returned by
When used in modeling, users should strongly consider using the
skip = TRUE so that the extra sampling is not
conducted outside of the training set.