mlr3 (version 0.14.1)

mlr_measures_bic: Bayesian Information Criterion Measure

Description

Calculates the Bayesian Information Criterion (BIC) which is a trade-off between goodness of fit (measured in terms of log-likelihood) and model complexity (measured in terms of number of included features). Internally, stats::BIC() is called. Requires the learner property "loglik", NA is returned for unsupported learners.

Dictionary

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

mlr_measures$get("bic")
msr("bic")

Meta Information

• Task type: “NA”

• Range: \((-\infty, \infty)\)

• Minimize: TRUE

• Average: macro

• Required Prediction: “response”

• Required Packages: mlr3

Parameters

Empty ParamSet

Super class

mlr3::Measure -> MeasureBIC

Methods

Public methods

• MeasureBIC$new()

• MeasureBIC$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

MeasureBIC$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

MeasureBIC$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

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_classif.costs, mlr_measures_debug, mlr_measures_elapsed_time, mlr_measures_oob_error, mlr_measures_selected_features, mlr_measures