mlr_measures_internal_valid_score: Measure Internal Validation Score

In mlr3: Machine Learning in R - Next Generation

Measure Internal Validation Score

Description

Returns the selected internal validation score of the Learner for learners property "validation". Returns NA for unsupported learners, when no validation was done, or when the selected id was not found. The id of this measure is set to the value of select if provided.

Dictionary

This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():

mlr_measures$get("internal_valid_score")
msr("internal_valid_score")

Meta Information

• Task type: “NA”

• Range: (-\infty, \infty)

• Minimize: NA

• Average: macro

• Required Prediction: “NA”

• Required Packages: mlr3

Parameters

Empty ParamSet

Super class

mlr3::Measure -> MeasureInternalValidScore

Methods

Public methods

• MeasureInternalValidScore$new()

• MeasureInternalValidScore$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

MeasureInternalValidScore$new(select = NULL, minimize = NA)

Arguments

select

(character(1))
Which of the internal validation scores to select. Which scores are available depends on the learner and its configuration. By default, the first score is chosen.

minimize

(logical(1))
Whether smaller values are better. Must be set to use for tuning.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

MeasureInternalValidScore$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

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_debug_classif, mlr_measures_elapsed_time, mlr_measures_oob_error, mlr_measures_regr.rsq, mlr_measures_selected_features

Examples

rr = resample(tsk("iris"), lrn("classif.debug", validate = 0.3), rsmp("holdout"))
rr$score(msr("internal_valid_score", select = "acc"))

mlr3 documentation built on Oct. 18, 2024, 5:11 p.m.