mlr3filters (version 0.4.0)

mlr_filters_auc: AUC Filter

Description

Area under the (ROC) Curve filter, analogously to mlr3measures::auc() from mlr3measures. Missing values of the features are removed before calculating the AUC. If the AUC is undefined for the input, it is set to 0.5 (random classifier). The absolute value of the difference between the AUC and 0.5 is used as final filter value.

Super class

mlr3filters::Filter -> FilterAUC

Methods

Public methods

• FilterAUC$new()

• FilterAUC$clone()

• mlr3filters::Filter$calculate()

• mlr3filters::Filter$format()

• mlr3filters::Filter$help()

• mlr3filters::Filter$print()

Method new()

Create a FilterAUC object.

Usage

FilterAUC$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

FilterAUC$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

Dictionary of Filters: mlr_filters

Other Filter: Filter, mlr_filters_anova, mlr_filters_carscore, 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_variance, mlr_filters

Examples

# NOT RUN {
task = mlr3::tsk("pima")
filter = flt("auc")
filter$calculate(task)
head(as.data.table(filter), 3)
# }