Debug Measure for Classification

This measure returns the number of observations in the PredictionClassif 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_classif")
msr("debug_classif")

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/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_elapsed_time, mlr_measures_oob_error, mlr_measures_selected_features

Super class

mlr3::Measure -> MeasureDebugClassif

Methods

Public methods

• MeasureDebugClassif$new()

• MeasureDebugClassif$clone()

Inherited methods

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

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Method new()

Creates a new instance of this R6 class.

Usage

MeasureDebugClassif$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

MeasureDebugClassif$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

task = tsk("wine")
learner = lrn("classif.featureless")
measure = msr("debug_classif", na_ratio = 0.5)
rr = resample(task, learner, rsmp("cv", folds = 5))
rr$score(measure)
#>    task_id          learner_id resampling_id iteration debug_classif
#>     <char>              <char>        <char>     <int>         <num>
#> 1:    wine classif.featureless            cv         1            36
#> 2:    wine classif.featureless            cv         2            NA
#> 3:    wine classif.featureless            cv         3            NA
#> 4:    wine classif.featureless            cv         4            35
#> 5:    wine classif.featureless            cv         5            NA
#> Hidden columns: task, learner, resampling, prediction