mlr3 (version 0.12.0)

mlr_measures_debug: Debug Measure

Description

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")

Parameters

Id	Type	Default	Range	Levels
normalize	logical	TRUE	\((-\infty, \infty)\)	TRUE, FALSE

Meta Information

• Type: NA

• Range: \([0, \infty)\)

• Minimize: NA

• Required prediction: 'response'

Super class

mlr3::Measure -> MeasureDebug

Methods

Public methods

• MeasureDebug$new()

• MeasureDebug$clone()

• 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.

See Also

• 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

Examples

# 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)
# }