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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: “NA”

  • Required Packages: mlr3

Parameters

Empty ParamSet

See also

Other Measure: Measure, MeasureClassif, MeasureRegr, MeasureSimilarity, mlr_measures, mlr_measures_aic, mlr_measures_classif.costs, mlr_measures_debug_classif, mlr_measures_elapsed_time, mlr_measures_oob_error, mlr_measures_selected_features

Super class

mlr3::Measure -> MeasureBIC

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


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.