mlr3 (version 0.12.0)

mlr_measures_aic: Akaike Information Criterion Measure

Description

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. 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")

Parameters

Id	Type	Default	Range	Levels
k	integer	-	\([0, \infty)\)	-

k is the penalty to be used in stats::AIC() (defaulting to 2).

Meta Information

• Type: NA

• Range: \((-\infty, \infty)\)

• Minimize: TRUE

• Required prediction: 'response'

• Learner Property: loglik

Super class

mlr3::Measure -> MeasureAIC

Methods

Public methods

• MeasureAIC$new()

• MeasureAIC$clone()

• mlr3::Measure$aggregate()

• mlr3::Measure$format()

• mlr3::Measure$help()

• mlr3::Measure$print()

• mlr3::Measure$score()

Method new()

Creates a new instance of this R6 class.

Usage

MeasureAIC$new()

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.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/train-predict.html

• 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, 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