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).
stats::AIC() is called.
Requires the learner property
NA is returned for unsupported learners.
k is the penalty to be used in
stats::AIC() (defaulting to 2).
Range: \((-\infty, \infty)\)
Required prediction: 'response'
Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#train-predict
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:
Creates a new instance of this R6 class.
The objects of this class are cloneable with this method.
MeasureAIC$clone(deep = FALSE)
Whether to make a deep clone.