step_nzv {recipes} | R Documentation

## Near-Zero Variance Filter

### Description

`step_nzv` creates a specification of a recipe step that will potentially remove variables that are highly sparse and unbalanced.

### Usage

    step_nzv(recipe, ..., role = NA, trained = FALSE,
      options = list(freq_cut = 95/5, unique_cut = 10), removals = NULL,
      skip = FALSE, id = rand_id("nzv"))

    ## S3 method for class 'step_nzv'
    tidy(x, ...)

### Arguments

`recipe` | A recipe object. The step will be added to the sequence of operations for this recipe.
`...` | One or more selector functions to choose which variables that will evaluated by the filtering. See `selections()` for more details. For the `tidy` method, these are not currently used.
`role` | Not used by this step since no new variables are created.
`trained` | A logical to indicate if the quantities for preprocessing have been estimated.
`options` | A list of options for the filter (see Details below).
`removals` | A character string that contains the names of columns that should be removed. These values are not determined until `prep.recipe()` is called.
`skip` | A logical. Should the step be skipped when the recipe is baked by `bake.recipe()`? While all operations are baked when `prep.recipe()` 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 = TRUE` as it may affect the computations for subsequent operations
`id` | A character string that is unique to this step to identify it.
`x` | A `step_nzv` object.

### Details

This step diagnoses predictors that have one unique value (i.e. are zero variance predictors) or predictors that are have both of the following characteristics:

1. they have very few unique values relative to the number of samples and

2. the ratio of the frequency of the most common value to the frequency of the second most common value is large.

For example, an example of near zero variance predictor is one that, for 1000 samples, has two distinct values and 999 of them are a single value.

To be flagged, first the frequency of the most prevalent value over the second most frequent value (called the "frequency ratio") must be above `freq_cut`. Secondly, the "percent of unique values," the number of unique values divided by the total number of samples (times 100), must also be below `unique_cut`.

In the above example, the frequency ratio is 999 and the unique value percentage is 0.0001.

### Value

An updated version of `recipe` with the new step added to the sequence of existing steps (if any). For the `tidy` method, a tibble with columns `terms` which is the columns that will be removed.

### See Also

`step_corr()` `recipe()` `prep.recipe()` `bake.recipe()`

### Examples

    data(biomass)

    biomass$sparse <- c(1, rep(0, nrow(biomass) - 1))

    biomass_tr <- biomass[biomass$dataset == "Training",]
    biomass_te <- biomass[biomass$dataset == "Testing",]

    rec <- recipe(HHV ~ carbon + hydrogen + oxygen +
                        nitrogen + sulfur + sparse,
                  data = biomass_tr)

    nzv_filter <- rec %>%
      step_nzv(all_predictors())

    filter_obj <- prep(nzv_filter, training = biomass_tr)

    filtered_te <- bake(filter_obj, biomass_te)
    any(names(filtered_te) == "sparse")

    tidy(nzv_filter, number = 1)
    tidy(filter_obj, number = 1)

[Package recipes version 0.1.5 Index]