Simple correlation filter calling stats::cor().
The filter score is the absolute value of the correlation.
Note
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.
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_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
Super class
Filter -> FilterCorrelation
Examples
## Pearson (default)
task = mlr3::tsk("mtcars")
filter = flt("correlation")
filter$calculate(task)
as.data.table(filter)
#> feature score
#> <char> <num>
#> 1: wt 0.8676594
#> 2: cyl 0.8521620
#> 3: disp 0.8475514
#> 4: hp 0.7761684
#> 5: drat 0.6811719
#> 6: vs 0.6640389
#> 7: am 0.5998324
#> 8: carb 0.5509251
#> 9: gear 0.4802848
#> 10: qsec 0.4186840
## Spearman
filter = FilterCorrelation$new()
filter$param_set$values = list("method" = "spearman")
filter$calculate(task)
as.data.table(filter)
#> feature score
#> <char> <num>
#> 1: cyl 0.9108013
#> 2: disp 0.9088824
#> 3: hp 0.8946646
#> 4: wt 0.8864220
#> 5: vs 0.7065968
#> 6: carb 0.6574976
#> 7: drat 0.6514555
#> 8: am 0.5620057
#> 9: gear 0.5427816
#> 10: qsec 0.4669358
if (mlr3misc::require_namespaces(c("mlr3pipelines", "rpart"), quietly = TRUE)) {
library("mlr3pipelines")
task = mlr3::tsk("mtcars")
# Note: `filter.frac` is selected randomly and should be tuned.
graph = po("filter", filter = flt("correlation"), filter.frac = 0.5) %>>%
po("learner", mlr3::lrn("regr.rpart"))
graph$train(task)
}
#> $regr.rpart.output
#> NULL
#>