  Bloom Filters are a fundamental and pervasive data structure. Within the
growing area of Learned Data Structures, several Learned versions of Bloom
Filters have been considered, yielding advantages over classic Filters. Each of
them uses a classifier, which is the Learned part of the data structure.
Although it has a central role in those new filters, and its space footprint as
well as classification time may affect the performance of the Learned Filter,
no systematic study of which specific classifier to use in which circumstances
is available. We report progress in this area here, providing also initial
guidelines on which classifier to choose among five classic classification
paradigms.
