mlr_filters_correlation | R Documentation |
Simple correlation filter calling stats::cor()
.
The filter score is the absolute value of the correlation.
mlr3filters::Filter
-> FilterCorrelation
new()
Create a FilterCorrelation object.
FilterCorrelation$new()
clone()
The objects of this class are cloneable with this method.
FilterCorrelation$clone(deep = FALSE)
deep
Whether to make a deep clone.
This filter, in its default settings, 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 value, the resulting score will be NA
.
Missing scores appear in a random, non-deterministic order at the end of the vector of scores.
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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.csda.2019.106839")}.
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_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
,
mlr_filters_variance
## Pearson (default)
task = mlr3::tsk("mtcars")
filter = flt("correlation")
filter$calculate(task)
as.data.table(filter)
## Spearman
filter = FilterCorrelation$new()
filter$param_set$values = list("method" = "spearman")
filter$calculate(task)
as.data.table(filter)
if (mlr3misc::require_namespaces(c("mlr3pipelines", "rpart"), quietly = TRUE)) {
library("mlr3pipelines")
task = mlr3::tsk("boston_housing")
# Note: `filter.frac` is selected randomly and should be tuned.
graph = po("filter", filter = flt("correlation"), filter.frac = 0.5) %>>%
po("learner", mlr3::lrn("regr.rpart"))
graph$train(task)
}
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