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.
Dictionary
This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr()
:
$get("debug")
mlr_measuresmsr("debug")
Meta Information
Task type: “NA”
Range: \([0, \infty)\)
Minimize: NA
Average: macro
Required Prediction: “response”
Required Packages: mlr3
See also
Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#train-predict
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
,
MeasureSimilarity
,
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
Super class
mlr3::Measure
-> MeasureDebug
Examples
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)
#> task task_id learner
#> 1: <TaskClassif[50]> wine <LearnerClassifFeatureless[38]>
#> 2: <TaskClassif[50]> wine <LearnerClassifFeatureless[38]>
#> 3: <TaskClassif[50]> wine <LearnerClassifFeatureless[38]>
#> 4: <TaskClassif[50]> wine <LearnerClassifFeatureless[38]>
#> 5: <TaskClassif[50]> wine <LearnerClassifFeatureless[38]>
#> learner_id resampling resampling_id iteration
#> 1: classif.featureless <ResamplingCV[20]> cv 1
#> 2: classif.featureless <ResamplingCV[20]> cv 2
#> 3: classif.featureless <ResamplingCV[20]> cv 3
#> 4: classif.featureless <ResamplingCV[20]> cv 4
#> 5: classif.featureless <ResamplingCV[20]> cv 5
#> prediction debug
#> 1: <PredictionClassif[20]> NA
#> 2: <PredictionClassif[20]> 36
#> 3: <PredictionClassif[20]> NA
#> 4: <PredictionClassif[20]> 35
#> 5: <PredictionClassif[20]> NA