Notes on the H-measure of classifier performance
David J. Hand, Christoforos Anagnostopoulos
Abstract
Abstract The H-measure is a classifier performance measure which takes into account the context of application without requiring a rigid value of relative misclassification costs to be set. Since its introduction in 2009 it has become widely adopted. This paper answers various queries which users have raised since its introduction, including questions about its interpretation, the choice of a weighting function, whether it is strictly proper, its coherence, and relates the measure to other work.
Topics & Concepts
WeightingClassifier (UML)Computer scienceMeasure (data warehouse)Coherence (philosophical gambling strategy)Artificial intelligenceMachine learningData miningMathematicsStatisticsRadiologyMedicineImbalanced Data Classification TechniquesFace and Expression RecognitionMachine Learning and Data Classification