Area under the (ROC) Curve filter, analogously to
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.
Create a FilterAUC object.
FilterAUC$new( id = "auc", task_type = "classif", task_properties = "twoclass", param_set = ParamSet$new(), packages = "mlr3measures", feature_types = c("integer", "numeric") )
Identifier for the filter.
Types of the task the filter can operator on. E.g.,
Set of hyperparameters.
The objects of this class are cloneable with this method.
FilterAUC$clone(deep = FALSE)
Whether to make a deep clone.
#> feature score #> 1: glucose 0.2927906 #> 2: insulin 0.2316288 #> 3: mass 0.1870358