mlr_filters_auc | R Documentation |
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$new()
clone()
The objects of this class are cloneable with this method.
FilterAUC$clone(deep = FALSE)
deep
Whether to make a deep clone.
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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.csda.2019.106839")}.
PipeOpFilter for filter-based feature selection.
Dictionary of Filters: mlr_filters
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
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)
}
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