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.
This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():
mlr_measures$get("aic")
msr("aic")
Task type: “NA”
Range: \((-\infty, \infty)\)
Minimize: TRUE
Average: macro
Required Prediction: “response”
Required Packages: mlr3
| Id | Type | Default | Range |
| k | integer | - | \([0, \infty)\) |
mlr3::Measure -> MeasureAIC
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:
mlr3proba for probabilistic supervised regression and survival analysis.
mlr3cluster for unsupervised clustering.
Other Measure:
MeasureClassif,
MeasureRegr,
MeasureSimilarity,
Measure,
mlr_measures_bic,
mlr_measures_classif.costs,
mlr_measures_debug,
mlr_measures_elapsed_time,
mlr_measures_oob_error,
mlr_measures_selected_features,
mlr_measures