step_corr {recipes} | R Documentation |
High correlation filter
Description
step_corr()
creates a specification of a recipe step that will
potentially remove variables that have large absolute correlations with other
variables.
Usage
step_corr(
recipe,
...,
role = NA,
trained = FALSE,
threshold = 0.9,
use = "pairwise.complete.obs",
method = "pearson",
removals = NULL,
skip = FALSE,
id = rand_id("corr")
)
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 variables
for this step. See |
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. |
threshold |
A value for the threshold of absolute correlation values. The step will try to remove the minimum number of columns so that all the resulting absolute correlations are less than this value. |
use |
A character string for the |
method |
A character string for the |
removals |
A character string that contains the names of
columns that should be removed. These values are not determined
until |
skip |
A logical. Should the step be skipped when the
recipe is baked by |
id |
A character string that is unique to this step to identify it. |
Details
This step can potentially remove columns from the data set. This may cause issues for subsequent steps in your recipe if the missing columns are specifically referenced by name. To avoid this, see the advice in the Tips for saving recipes and filtering columns section of selections.
This step attempts to remove variables to keep the
largest absolute correlation between the variables less than
threshold
.
When a column has a single unique value, that column will be
excluded from the correlation analysis. Also, if the data set
has sporadic missing values (and an inappropriate value of use
is chosen), some columns will also be excluded from the filter.
The arguments use
and method
don't take effect if case weights
are used in the recipe.
Value
An updated version of recipe
with the new step added to the
sequence of any existing operations.
Tidying
When you tidy()
this step, a tibble is returned with
columns terms
and id
:
- terms
character, the selectors or variables selected to be removed
- id
character, id of this step
Tuning Parameters
This step has 1 tuning parameters:
-
threshold
: Threshold (type: double, default: 0.9)
Case weights
This step performs an unsupervised operation that can utilize case weights.
As a result, case weights are only used with frequency weights. For more
information, see the documentation in case_weights and the examples on
tidymodels.org
.
Author(s)
Original R code for filtering algorithm by Dong Li,
modified by Max Kuhn. Contributions by Reynald Lescarbeau (for
original in caret
package). Max Kuhn for the step
function.
See Also
Other variable filter steps:
step_filter_missing()
,
step_lincomb()
,
step_nzv()
,
step_rm()
,
step_select()
,
step_zv()
Examples
data(biomass, package = "modeldata")
set.seed(3535)
biomass$duplicate <- biomass$carbon + rnorm(nrow(biomass))
biomass_tr <- biomass[biomass$dataset == "Training", ]
biomass_te <- biomass[biomass$dataset == "Testing", ]
rec <- recipe(
HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur + duplicate,
data = biomass_tr
)
corr_filter <- rec %>%
step_corr(all_numeric_predictors(), threshold = .5)
filter_obj <- prep(corr_filter, training = biomass_tr)
filtered_te <- bake(filter_obj, biomass_te)
round(abs(cor(biomass_tr[, c(3:7, 9)])), 2)
round(abs(cor(filtered_te)), 2)
tidy(corr_filter, number = 1)
tidy(filter_obj, number = 1)