Can someone give a good math/stats explanation as to what the parameter var_smoothing does for GaussianNB in scikit learn?
A Gaussian curve can serve as a "low pass" filter, allowing only the samples close to its mean to "pass." In the context of Naive Bayes, assuming a Gaussian distribution is essentially giving more weights to the samples closer to the distribution mean. This might or might not be appropriate depending if what you want to predict follows a normal distribution.
The variable, var_smoothing, artificially adds a user-defined value to the distribution's variance (whose default value is derived from the training data set). This essentially widens (or "smooths") the curve and accounts for more samples that are further away from the distribution mean.
I am aware of this parameter var_smoothing and how to tune it, but I'd like an explanation from a math/stats aspect that explains what tuning it actually does - I haven't been able to find any good ones online.