mlr_filters_carscore | R Documentation |
Calculates the Correlation-Adjusted (marginal) coRrelation scores
(short CAR scores) implemented in care::carscore()
in package
care. The CAR scores for a set of features are defined as the
correlations between the target and the decorrelated features. The filter
returns the absolute value of the calculated scores.
Argument verbose
defaults to FALSE
.
mlr3filters::Filter
-> FilterCarScore
new()
Create a FilterCarScore object.
FilterCarScore$new()
clone()
The objects of this class are cloneable with this method.
FilterCarScore$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_anova
,
mlr_filters_auc
,
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_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
if (requireNamespace("care")) { task = mlr3::tsk("mtcars") filter = flt("carscore") filter$calculate(task) head(as.data.table(filter), 3) ## changing the filter settings filter = flt("carscore") filter$param_set$values = list("diagonal" = TRUE) filter$calculate(task) head(as.data.table(filter), 3) } if (mlr3misc::require_namespaces(c("mlr3pipelines", "care", "rpart"), quietly = TRUE)) { library("mlr3pipelines") task = mlr3::tsk("mtcars") # Note: `filter.frac` is selected randomly and should be tuned. graph = po("filter", filter = flt("carscore"), filter.frac = 0.5) %>>% po("learner", mlr3::lrn("regr.rpart")) graph$train(task) }
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