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():

### 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.