A two-tiered 2D visual tool for assessing classifier performance

In this article, a new kind of 2D tool is proposed, namely ⟨φ δ⟩ diagrams, able to highlight most of the information deemed relevant for classifier building and assessment. In particular, accuracy, bias and break-even points are immediately evident therein. These diagrams come in two different forms: the first is aimed at representing the phenomenon under investigation in a space where the imbalance between negative and positive samples is not taken into account, the second (which is a generalization of the first) is able to visualize relevant information in a space that accounts also for the imbalance. According to a specific design choice, all properties found in the first space hold also in the second. The combined use of φ and δ can give important information to researchers involved in the activity of building intelligent systems, in particular for classifier performance assessment and feature ranking/selection.