Describe the workflow you want to enable

The idea seems simple enough as described in this paper: https://arxiv.org/pdf/1905.09884.pdf

Due to its linear complexity, naive Bayes classification remains an attractive su-
pervised learning method, especially in very large-scale settings. We propose a
sparse version of naive Bayes, which can be used for feature selection. This leads
to a combinatorial maximum-likelihood problem, for which we provide an exact
solution in the case of binary data, or a bound in the multinomial case. We prove
that our bound becomes tight as the marginal contribution of additional features
decreases. Both binary and multinomial sparse models are solvable in time almost
linear in problem size, representing a very small extra relative cost compared to
the classical naive Bayes. Numerical experiments on text data show that the naive
Bayes feature selection method is as statistically effective as state-of-the-art feature
selection methods such as recursive feature elimination, l1-penalized logistic re-
gression and LASSO, while being orders of magnitude faster. For a large data set,
having more than with 1.6 million training points and about 12 million features,
and with a non-optimized CPU implementation, our sparse naive Bayes model can
be trained in less than 15 seconds.

Describe your proposed solution

Add a regularization parameter to the current implementation of BernoulliNB that allows to control sparsity of the solution similar to regularization strength or "C" in LogisticRegression with a L1 penalty.

Make the necessary adjustments to the algorithm.