The H-measure is a coherent alternative to the Area Under the ROC Curve (AUC) for measuring classification performance. Authors: David J. Hand, Christoforos Anagnostopoulos (maintainer).

Above code is still in beta mode -- please report any bugs to info [at] hmeasure [dot] net. References:
  • Hand, D.J. and Anagnostopoulos, C., A better Beta for the H measure of classification performance, arXiv preprint, http://arxiv.org/abs/1202.2564
  • D.J. Hand. Measuring classifier performance: a coherent alternative to the area under the ROC curve. Machine Learning, 77:103–123, 2009.
  • D.J. Hand. Evaluating diagnostic tests: the area under the ROC curve and the balance of errors. Statistics in Medicine, 29:1502–1510, 2010.



The H-measure in one picture: The H measure can be motivated in terms of a prior on the severity of the two types of misclassification costs (false alarms versus missed positive cases). Under this interpretation, the prior weights implicitly imposed on the relative misclassification cost by the popular Area Under the Curve (see leftmost plot) are classifier-dependent (rightmost plot), whereas in the case of the H-measure they can be controlled explicitly using a Beta prior (centre plot).