Gain, which is the sum of the graded relevance values of all results in the list of retrieved objects of a given query. The definition of DCG adopted in this paper can be found in [@Jarvelin2002].

#### Precision-Recall, mAP and e-measure {#precision-recall-map-and-e-measure .unnumbered}

The *Precision* and *Recall* are common measures for retrieval evaluation. Recall is the ratio of the number of relevant records retrieved to the total number of relevant records, while precision is the ratio of the number of relevant records retrieved to the size of the return vector [@salton_evaluation]. Precision and recall always range from 0 to 1. Often, precision and recall are plot as a curve in the reference frame recall vs. precision [@Baeza-Yates:1999]: the larger the area below such a curve, the better the performance under examination. As an additional index, we consider the mean Average Precision (mAP), which is the portion of area under a precision-recall curve. Finally, we consider the *e-measure* $e$ [@Rijsbergen1979], which is a quality measure of the first models retrieved for every query. The e-measure depends on the *Precision* and *Recall* values by the relation: $e=\frac{2}{Precision^{-1}+Recall^{-1}}.$

#### Confusion matrices and Tier images {#confusion-matrices-and-tier-images .unnumbered}

Each classification performance can be associated with 