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.
mlr3filters::Filter
-> FilterAUC
new()
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") )
id
(character(1)
)
Identifier for the filter.
task_type
(character()
)
Types of the task the filter can operator on. E.g., "classif"
or
"regr"
.
task_properties
(character()
)
Required task properties, see mlr3::Task.
Must be a subset of
mlr_reflections$task_properties
.
param_set
(paradox::ParamSet) Set of hyperparameters.
packages
(character()
)
Set of required packages.
Note that these packages will be loaded via requireNamespace()
, and
are not attached.
feature_types
(character()
)
Feature types the filter operates on.
Must be a subset of
mlr_reflections$task_feature_types
.
clone()
The objects of this class are cloneable with this method.
FilterAUC$clone(deep = FALSE)
deep
Whether to make a deep clone.
Dictionary of Filters: mlr_filters
Other Filter:
Filter
,
mlr_filters_anova
,
mlr_filters_carscore
,
mlr_filters_cmim
,
mlr_filters_correlation
,
mlr_filters_disr
,
mlr_filters_find_correlation
,
mlr_filters_importance
,
mlr_filters_information_gain
,
mlr_filters_jmim
,
mlr_filters_jmi
,
mlr_filters_kruskal_test
,
mlr_filters_mim
,
mlr_filters_mrmr
,
mlr_filters_njmim
,
mlr_filters_performance
,
mlr_filters_permutation
,
mlr_filters_variance
,
mlr_filters
# NOT RUN {
task = mlr3::tsk("pima")
filter = flt("auc")
filter$calculate(task)
head(as.data.table(filter), 3)
# }
Run the code above in your browser using DataLab