  The property of conformal predictors to guarantee the required accuracy rate
makes this framework attractive in various practical applications. However,
this property is achieved at a price of reduction in precision. In the case of
conformal classification, the systems can output multiple class labels instead
of one. It is also known from the literature, that the choice of nonconformity
function has a major impact on the efficiency of conformal classifiers.
Recently, it was shown that different model-agnostic nonconformity functions
result in conformal classifiers with different characteristics. For a Neural
Network-based conformal classifier, the inverse probability (or hinge loss)
allows minimizing the average number of predicted labels, and margin results in
a larger fraction of singleton predictions. In this work, we aim to further
extend this study. We perform an experimental evaluation using 8 different
classification algorithms and discuss when the previously observed relationship
holds or not. Additionally, we propose a successful method to combine the
properties of these two nonconformity functions. The experimental evaluation is
done using 11 real and 5 synthetic datasets.
