mlr_filters_find_correlation | R Documentation |
Simple filter emulating caret::findCorrelation(exact = FALSE)
.
This gives each feature a score between 0 and 1 that is one minus the
cutoff value for which it is excluded when using caret::findCorrelation()
.
The negative is used because caret::findCorrelation()
excludes everything
above a cutoff, while filters exclude everything below a cutoff.
Here the filter scores are shifted by +1 to get positive values for to align
with the way other filters work.
Subsequently caret::findCorrelation(cutoff = 0.9)
lists the same features
that are excluded with FilterFindCorrelation
at score 0.1 (= 1 - 0.9).
mlr3filters::Filter
-> FilterFindCorrelation
new()
Create a FilterFindCorrelation object.
FilterFindCorrelation$new()
clone()
The objects of this class are cloneable with this method.
FilterFindCorrelation$clone(deep = FALSE)
deep
Whether to make a deep clone.
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_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
,
mlr_filters_variance
# Pearson (default)
task = mlr3::tsk("mtcars")
filter = flt("find_correlation")
filter$calculate(task)
as.data.table(filter)
## Spearman
filter = flt("find_correlation", method = "spearman")
filter$calculate(task)
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("find_correlation"), filter.frac = 0.5) %>>%
po("learner", mlr3::lrn("classif.rpart"))
graph$train(task)
}
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