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#' @title Akaike Information Criterion Measure
#'
#' @name mlr_measures_aic
#' @include Measure.R
#'
#' @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 with parameter `k` (defaulting to 2).
#' Requires the learner property `"loglik"`, `NA` is returned for unsupported learners.
#'
#' @templateVar id aic
#' @template measure
#'
#' @template seealso_measure
#' @export
MeasureAIC = R6Class("MeasureAIC",
inherit = Measure,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
param_set = ps(k = p_int(lower = 0))
super$initialize(
id = "aic",
task_type = NA_character_,
param_set = param_set,
predict_sets = NULL,
properties = c("na_score", "requires_learner", "requires_model", "requires_no_prediction"),
predict_type = NA_character_,
minimize = TRUE,
label = "Akaike Information Criterion",
man = "mlr3::mlr_measures_aic"
)
}
),
private = list(
.score = function(prediction, learner, ...) {
learner = learner$base_learner()
if ("loglik" %nin% learner$properties) {
return(NA_real_)
}
k = self$param_set$values$k %??% 2
return(stats::AIC(learner$loglik(), k = k))
}
)
)
#' @include mlr_measures.R
mlr_measures$add("aic", function() MeasureAIC$new())
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