  Non-local correlations that obey the no-signalling principle contain
intrinsic randomness. In particular, for a specific Bell experiment, one can
derive relations between the amount of randomness produced, as quantified by
the min-entropy of the output data, and its associated violation of a Bell
inequality. In practice, due to finite sampling, certifying randomness requires
the development of statistical tools to lower-bound the min-entropy of the data
as a function of the estimated Bell violation. The quality of such bounds
relies on the choice of certificate, i.e., the Bell inequality whose violation
is estimated. In this work, we propose a method for choosing efficiently such a
certificate. It requires sacrificing a part of the output data in order to
estimate the underlying correlations. Regularising this estimate then allows
one to find a Bell inequality that is well suited for certifying practical
randomness from these specific correlations. We then study the effects of
various parameters on the obtained min-entropy bound and explain how to tune
them in a favourable way. Lastly, we carry out several numerical simulations of
a Bell experiment to show the efficiency of our method: we nearly always obtain
higher min-entropy rates than when we use a pre-established Bell inequality,
namely the Clauser-Horne-Shimony-Holt inequality.
