mlr_filters_auc {mlr3filters} | R Documentation |
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
Inherited methods
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.
References
For a benchmark of filter methods:
Bommert A, Sun X, Bischl B, Rahnenführer J, Lang M (2020). “Benchmark for filter methods for feature selection in high-dimensional classification data.” Computational Statistics & Data Analysis, 143, 106839. doi:10.1016/j.csda.2019.106839.
See Also
-
PipeOpFilter for filter-based feature selection.
Other Filter:
Filter
,
mlr_filters
,
mlr_filters_anova
,
mlr_filters_boruta
,
mlr_filters_carscore
,
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_jmi
,
mlr_filters_jmim
,
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_univariate_cox
,
mlr_filters_variance
Examples
task = mlr3::tsk("sonar")
filter = flt("auc")
filter$calculate(task)
head(as.data.table(filter), 3)
if (mlr3misc::require_namespaces(c("mlr3pipelines", "rpart"), quietly = TRUE)) {
library("mlr3pipelines")
task = mlr3::tsk("spam")
# Note: `filter.frac` is selected randomly and should be tuned.
graph = po("filter", filter = flt("auc"), filter.frac = 0.5) %>>%
po("learner", mlr3::lrn("classif.rpart"))
graph$train(task)
}