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Calculates the Akaike Information Criterion (AIC) 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::AIC() is called with parameter k (defaulting to 2). 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("aic")
msr("aic")

Meta Information

  • Task type: “NA”

  • Range: \((-\infty, \infty)\)

  • Minimize: TRUE

  • Average: macro

  • Required Prediction: “response”

  • Required Packages: mlr3

Parameters

IdTypeDefaultRange
kinteger-\([0, \infty)\)

See also

Other Measure: MeasureClassif, MeasureRegr, MeasureSimilarity, Measure, mlr_measures_bic, 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 -> MeasureAIC

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

MeasureAIC$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.