mlr_measures_aic function

Akaike Information Criterion Measure

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: $(-Inf, Inf)$
• Minimize: TRUE
• Average: macro
• Required Prediction: NA
• Required Packages: list("mlr3")

Parameters

Id	Type	Default	Range
k	integer	-	$[0, Inf)$

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-eval

• Package list("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:

  • list("mlr3proba") for probabilistic supervised regression and survival analysis.
  • list("mlr3cluster") for unsupervised clustering.

Other Measure: Measure, MeasureClassif, MeasureRegr, MeasureSimilarity, mlr_measures, mlr_measures_bic, mlr_measures_classif.costs, mlr_measures_debug_classif, mlr_measures_elapsed_time, mlr_measures_internal_valid_score, mlr_measures_oob_error, mlr_measures_regr.rsq, mlr_measures_selected_features

Super class

mlr3::Measure -> MeasureAIC

Methods

Public methods

• list("MeasureAIC$new()")

• list("MeasureAIC$clone()")

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.