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Evaluation often aims to reduce the correctness or error characteristics of asystem down to a single number, but that always involves trade-offs. Anotherway of dealing with this is to quote two numbers, such as Recall and Precision,or Sensitivity and Specificity. But it can also be useful to see more thanthis, and a graphical approach can explore sensitivity to cost, prevalence,bias, noise, parameters and hyper-parameters.  Moreover, most techniques are implicitly based on two balanced classes, andour ability to visualize graphically is intrinsically two dimensional, but weoften want to visualize in a multiclass context. We review the dichotomousapproaches relating to Precision, Recall, and ROC as well as the related LIFTchart, exploring how they handle unbalanced and multiclass data, and derivingnew probabilistic and information theoretic variants of LIFT that help dealwith the issues associated with the handling of multiple and unbalancedclasses.

Visualization of Tradeoff in Evaluation: from Precision-Recall & PN to LIFT, ROC & BIRD