mlr_filters_relief | R Documentation |
Information gain filter calling
FSelectorRcpp::relief()
in package FSelectorRcpp.
mlr3filters::Filter
-> FilterRelief
new()
Create a FilterRelief object.
FilterRelief$new()
clone()
The objects of this class are cloneable with this method.
FilterRelief$clone(deep = FALSE)
deep
Whether to make a deep clone.
This filter can handle missing values in the features. However, the resulting filter scores may be misleading or at least difficult to compare if some features have a large proportion of missing values.
If a feature has no non-missing observation, the resulting score will be (close to) 0.
PipeOpFilter for filter-based feature selection.
Dictionary of Filters: mlr_filters
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_selected_features
,
mlr_filters_univariate_cox
,
mlr_filters_variance
if (requireNamespace("FSelectorRcpp")) {
## Relief (default)
task = mlr3::tsk("iris")
filter = flt("relief")
filter$calculate(task)
head(filter$scores, 3)
as.data.table(filter)
}
if (mlr3misc::require_namespaces(c("mlr3pipelines", "FSelectorRcpp", "rpart"), quietly = TRUE)) {
library("mlr3pipelines")
task = mlr3::tsk("iris")
# Note: `filter.frac` is selected randomly and should be tuned.
graph = po("filter", filter = flt("relief"), filter.frac = 0.5) %>>%
po("learner", mlr3::lrn("classif.rpart"))
graph$train(task)
}
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