  Noting the erroneous proclivity of information-theoretic approaches, like the
Akaike information criterion (AIC), to select simpler models while performing
model selection with a small sample size, we address the problem of new physics
model selection in $b\to c \tau \nu_{\tau}$ decays in this paper by employing a
specific machine learning algorithm (self-normalizing neural networks, a.k.a.
SNN) for supervised classification and regression, in a model-independent
framework. While the outcomes of the classification with real data-set are
compared with AIC, with the SNNs outperforming AIC$_c$ in all aspects of model
selection, the regression-outcomes are compared with the results from Bayesian
analyses; the obtained parameter spaces differ considerably while keeping
maximum posterior (MAP) estimates similar. A few of the two-operator scenarios
with a tensor-type interaction are found to be the most probable solution for
the data. We also test the effectiveness of our trained networks with the
expected, more precise data in Belle-II. The trained networks and associated
functionalities are supplied for the use of the community.
