  This paper explores the calibration of a classifier output score in binary
classification problems. A calibrator is a function that maps the arbitrary
classifier score, of a testing observation, onto $[0,1]$ to provide an estimate
for the posterior probability of belonging to one of the two classes.
Calibration is important for two reasons; first, it provides a meaningful
score, that is the posterior probability; second, it puts the scores of
different classifiers on the same scale for comparable interpretation. The
paper presents three main contributions: (1) Introducing multi-score
calibration, when more than one classifier provides a score for a single
observation. (2) Introducing the idea that the classifier scores to a
calibration process are nothing but features to a classifier, hence proposing
expanding the classifier scores to higher dimensions to boost the calibrator's
performance. (3) Conducting a massive simulation study, in the order of 24,000
experiments, that incorporates different configurations, in addition to
experimenting on two real datasets from the cybersecurity domain. The results
show that there is no overall winner among the different calibrators and
different configurations. However, general advices for practitioners include
the following: the Platt's
calibrator~\citep{Platt1999ProbabilisticOutputsForSupport}, a version of the
logistic regression that decreases bias for a small sample size, has a very
stable and acceptable performance among all experiments; our suggested
multi-score calibration provides better performance than single score
calibration in the majority of experiments, including the two real datasets. In
addition, expanding the scores can help in some experiments.
