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.

Format

R6::R6Class inheriting from Filter.

Construction

FilterAUC$new()
mlr_filters$get("auc")
flt("auc")

See also

Examples

task = mlr3::tsk("pima") filter = flt("auc") filter$calculate(task) head(as.data.table(filter), 3)
#> feature score #> 1: glucose 0.2927906 #> 2: insulin 0.2316288 #> 3: mass 0.1870358