Debug Measure

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():

mlr_measures$get("debug")
msr("debug")

Meta Information

• Task type: “NA”

• Range: \([0, \infty)\)

• Minimize: NA

• Average: macro

• Required Prediction: “response”

• Required Packages: mlr3

Parameters

Id	Type	Default	Range
na_ratio	numeric	-	\([0, 1]\)

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

Methods

Public methods

• MeasureDebug$new()

• MeasureDebug$clone()

Inherited methods

• mlr3::Measure$aggregate()
• mlr3::Measure$format()
• mlr3::Measure$help()
• mlr3::Measure$print()
• mlr3::Measure$score()

---

Method new()

Creates a new instance of this R6 class.

Usage

MeasureDebug$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

MeasureDebug$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

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