  Channel modeling is a critical issue when designing or evaluating the
performance of reconfigurable intelligent surface (RIS)-assisted
communications. Inspired by the promising potential of learning-based methods
for characterizing the radio environment, we present a general approach to
model the RIS end-to-end equivalent channel using the unsupervised
expectation-maximization (EM) learning algorithm. We show that an EM-based
approximation through a simple mixture of two Nakagami-$m$ distributions
suffices to accurately approximating the equivalent channel, while allowing for
the incorporation of crucial aspects into RIS's channel modeling as spatial
channel correlation, phase-shift errors, arbitrary fading conditions, and
coexistence of direct and RIS channels. Based on the proposed analytical
framework, we evaluate the outage probability under different settings of RIS's
channel features and confirm the superiority of this approach compared to
recent results in the literature.
