Akaike Information Criterion MeasureSource:
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 with parameter
k (defaulting to 2).
Requires the learner property
NA is returned for unsupported learners.
Task type: “NA”
Range: \((-\infty, \infty)\)
Required Prediction: “response”
Required Packages: mlr3
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: