Measures the proportion of true values that fall within the prediction interval. A well-calibrated prediction interval at level \(1 - \alpha\) should have coverage close to \(1 - \alpha\).
Details
$$ \mathrm{Coverage} = \frac{1}{n} \sum_{i=1}^n \mathbf{1}\{l_i \le y_i \le u_i\} $$ where \(l_i\) and \(u_i\) are the lower and upper bounds of the prediction interval and \(y_i\) is the observed value.
Dictionary
This mlr3::Measure can be instantiated via the dictionary mlr3::mlr_measures or with the associated sugar function mlr3::msr():
Meta Information
Task type: “regr”
Range: \([0, 1]\)
Minimize: FALSE
Average: macro
Required Prediction: “quantiles”
Required Packages: mlr3, mlr3forecast
See also
Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-eval
Package mlr3measures for the scoring functions.
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:
mlr_measures_fcst.acf1,
mlr_measures_fcst.mase,
mlr_measures_fcst.mda,
mlr_measures_fcst.mdpv,
mlr_measures_fcst.mdv,
mlr_measures_fcst.mpe,
mlr_measures_fcst.rmsse,
mlr_measures_fcst.winkler
Super classes
mlr3::Measure -> mlr3::MeasureRegr -> MeasureCoverage