| 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
deepWhether 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)
}