mlr3filters (version 0.7.1)

mlr_filters_correlation: Correlation Filter

Description

Simple correlation filter calling stats::cor(). The filter score is the absolute value of the correlation.

Super class

mlr3filters::Filter -> FilterCorrelation

Methods

Public methods

• FilterCorrelation$new()

• FilterCorrelation$clone()

Inherited methods

• mlr3filters::Filter$calculate()
• mlr3filters::Filter$format()
• mlr3filters::Filter$help()
• mlr3filters::Filter$print()

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Method new()

Create a FilterCorrelation object.

Usage

FilterCorrelation$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

FilterCorrelation$clone(deep = FALSE)

Arguments

deep

Whether 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. tools:::Rd_expr_doi("10.1016/j.csda.2019.106839").

See Also

• PipeOpFilter for filter-based feature selection.

• Dictionary of Filters: mlr_filters

Other Filter: Filter, mlr_filters_anova, mlr_filters_auc, 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_jmim, mlr_filters_jmi, 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_variance, mlr_filters

Examples

## 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)
}