mlr_measures_selected_features | R Documentation |
Measures the number of selected features by extracting it from learners with property "selected_features"
.
If parameter normalize
is set to TRUE
, the relative number of features instead of the absolute
number of features is returned.
Note that the models must be stored to be able to extract this information.
If the learner does not support the extraction of used features, NA
is returned.
This measure requires the Task and the Learner for scoring.
This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr()
:
mlr_measures$get("selected_features") msr("selected_features")
Task type: “NA”
Range: [0, \infty)
Minimize: TRUE
Average: macro
Required Prediction: “NA”
Required Packages: mlr3
Id | Type | Default | Levels |
normalize | logical | - | TRUE, FALSE |
mlr3::Measure
-> MeasureSelectedFeatures
new()
Creates a new instance of this R6 class.
MeasureSelectedFeatures$new()
clone()
The objects of this class are cloneable with this method.
MeasureSelectedFeatures$clone(deep = FALSE)
deep
Whether to make a deep clone.
Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-eval
Package mlr3measures for the scoring functions.
Dictionary of Measures: mlr_measures
as.data.table(mlr_measures)
for a table of available Measures in the running session (depending on the loaded packages).
Extension packages for additional task types:
mlr3proba for probabilistic supervised regression and survival analysis.
mlr3cluster for unsupervised clustering.
Other Measure:
Measure
,
MeasureClassif
,
MeasureRegr
,
MeasureSimilarity
,
mlr_measures
,
mlr_measures_aic
,
mlr_measures_bic
,
mlr_measures_classif.costs
,
mlr_measures_debug_classif
,
mlr_measures_elapsed_time
,
mlr_measures_internal_valid_score
,
mlr_measures_oob_error
,
mlr_measures_regr.rsq
task = tsk("german_credit")
learner = lrn("classif.rpart")
rr = resample(task, learner, rsmp("cv", folds = 3), store_models = TRUE)
scores = rr$score(msr("selected_features"))
scores[, c("iteration", "selected_features")]
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.