  In this work we cast the problem of binary classification in terms of
estimating a partition on Bernoulli data. When the explanatory variables are
all categorical, the problem can be modelled using the language of boolean
functions. We offer a probabilistic analysis of the problem, and propose two
algorithms for learning boolean functions from binary data.
