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This measure returns the number of observations in the Prediction object.
Its main purpose is debugging.
The parameter na_ratio
(numeric(1)
) controls the ratio of scores which randomly
are set to NA
, between 0 (default) and 1.
This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr()
:
mlr_measures$get("debug") msr("debug")
Id | Type | Default | Range | Levels |
normalize | logical | TRUE | |
TRUE, FALSE |
Type: NA
Range:
Minimize: NA
Required prediction: 'response'
mlr3::Measure
-> MeasureDebug
new()
Creates a new instance of this R6 class.
MeasureDebug$new()
clone()
The objects of this class are cloneable with this method.
MeasureDebug$clone(deep = FALSE)
deep
Whether to make a deep clone.
Chapter in the mlr3book: https://mlr3book.mlr-org.com/train-predict.html
Package mlr3measures for the scoring functions.
Dictionary of Measures: mlr_measures
as.data.table(mlr_measures)
for a table of available Measures in the running session (depending on the loaded packages).
Extension packages for additional task types:
mlr3proba for probabilistic supervised regression and survival analysis.
mlr3cluster for unsupervised clustering.
Other Measure:
MeasureClassif
,
MeasureRegr
,
Measure
,
mlr_measures_aic
,
mlr_measures_bic
,
mlr_measures_classif.costs
,
mlr_measures_elapsed_time
,
mlr_measures_oob_error
,
mlr_measures_selected_features
,
mlr_measures
# NOT RUN {
task = tsk("wine")
learner = lrn("classif.featureless")
measure = msr("debug", na_ratio = 0.5)
rr = resample(task, learner, rsmp("cv", folds = 5))
rr$score(measure)
# }
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