List of measures  |  Examples
Measures (metrics) for statistics and machine learning


This package defines common measures (metrics) for classification and regression problems in statistics and machine learning. To see if your favorite measure is implemented, see this list. Some multi-target measures are included, but see also Custom multi-target measures.

Measures with parameters (e.g., the $L^p$ loss) are realized as callable instances of a struct; calling syntax complies with the specification in StatisticalMeasuresBase.jl.

In addition to the measures themselves, this package provides:

  • A tool roc_curve for plotting Receiver Operator Characteristics

  • An extension module allowing measures from LossFunctions.jl to be used and extended using the same syntax as other measures. See Using losses from LossFunctions.jl.

  • A submodule ConfusionMatrices providing a confusion matrix type and basic functionality.

  • A submodule Functions where some core measure implementations are factored out as pure functions.