David J. Hand

David J Hand; David Hand’s contribution to the Discussion of ‘Independent Review of the UK Statistics Authority’ by Denise Lievesley, Journal of the Royal

Methods to classify objects into two or more classes are at the core of various disciplines. When a set of objects with their true classes is available, a supervised classifier can be trained and employed to decide if, for example, a new patient has cancer or not. The choice of performance measure is critical in deciding which supervised method to use in any particular classification problem. Different measures can lead to very different choices, so the measure should match the objectives. Many performance measures have been developed, and one of them is the F-measure, the harmonic mean of precision and recall. Originally proposed in information retrieval, the F-measure has gained increasing interest in the context of classification. However, the rationale underlying this measure appears weak, and unlike other measures, it does not have a representational meaning. The use of the harmonic mean also has little theoretical justification. The F-measure also stresses one class, which seems inappropriate for general classification problems. We provide a history of the F-measure and its use in computational disciplines, describe its properties, and discuss criticism about the F-Measure. We conclude with alternatives to the F-measure, and recommendations of how to use it effectively.

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.

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, and its coherence, and relates the measure to other work.

The F-measure, also known as the F1-score, is widely used to assess the performance of classification algorithms. However, some researchers find it lacking in intuitive interpretation, questioning the appropriateness of combining two aspects of performance as conceptually distinct as precision and recall, and also questioning whether the harmonic mean is the best way to combine them. To ease this concern, we describe a simple transformation of the F-measure, which we call F* (F-star), which has an immediate practical interpretation.

Recent years have seen dramatic changes in sources of data, amounts of data, availability of data, frequency of data, and types of data. Along with advances in data analytic technology these changes have opened up huge possibilities for improving the