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

  • Type: NA

  • Range: \((-\infty, \infty)\)

  • Minimize: TRUE

  • Required prediction: 'response'

  • Learner Property: loglik

See also

Other Measure: MeasureClassif, MeasureRegr, 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

Super class

mlr3::Measure -> MeasureBIC

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

MeasureBIC$new()


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.