mlr_filters_variance {mlr3filters} | R Documentation |
Variance Filter
Description
Variance filter calling stats::var()
.
Argument na.rm
defaults to TRUE
here.
Super class
mlr3filters::Filter
-> FilterVariance
Methods
Public methods
Inherited methods
Method new()
Create a FilterVariance object.
Usage
FilterVariance$new()
Method clone()
The objects of this class are cloneable with this method.
Usage
FilterVariance$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.
References
For a benchmark of filter methods:
Bommert A, Sun X, Bischl B, Rahnenführer J, Lang M (2020). “Benchmark for filter methods for feature selection in high-dimensional classification data.” Computational Statistics & Data Analysis, 143, 106839. doi:10.1016/j.csda.2019.106839.
See Also
-
PipeOpFilter for filter-based feature selection.
Other Filter:
Filter
,
mlr_filters
,
mlr_filters_anova
,
mlr_filters_auc
,
mlr_filters_boruta
,
mlr_filters_carscore
,
mlr_filters_carsurvscore
,
mlr_filters_cmim
,
mlr_filters_correlation
,
mlr_filters_disr
,
mlr_filters_find_correlation
,
mlr_filters_importance
,
mlr_filters_information_gain
,
mlr_filters_jmi
,
mlr_filters_jmim
,
mlr_filters_kruskal_test
,
mlr_filters_mim
,
mlr_filters_mrmr
,
mlr_filters_njmim
,
mlr_filters_performance
,
mlr_filters_permutation
,
mlr_filters_relief
,
mlr_filters_selected_features
,
mlr_filters_univariate_cox
Examples
task = mlr3::tsk("mtcars")
filter = flt("variance")
filter$calculate(task)
head(filter$scores, 3)
as.data.table(filter)
if (mlr3misc::require_namespaces(c("mlr3pipelines", "rpart"), quietly = TRUE)) {
library("mlr3pipelines")
task = mlr3::tsk("spam")
# Note: `filter.frac` is selected randomly and should be tuned.
graph = po("filter", filter = flt("variance"), filter.frac = 0.5) %>>%
po("learner", mlr3::lrn("classif.rpart"))
graph$train(task)
}