Thanks Andy, I can comprehend to the point "...and then sample from these Bernoulli distributions"
>From the data in `feature_log_prob_`, I would guess it contains single feature (features mean from the trained data) for each class. I can see how can I sample from `feature_log_prob_`... On Mon, Oct 3, 2016 at 3:07 PM, Andreas Mueller <t3k...@gmail.com> wrote: > Hi Klo. > Yes, you could, but as the model is very simple, that's usually not very > interesting. > It stores for each label an independent Bernoulli distribution for each > feature. > these are stored in feature_log_prob_. > I would suggest you look at this attribute, rather than sample from the > distribution. > To sample from it you would have to exponentiate it and then sample from > these Bernoulli distributions. > > Andy > > > On 10/03/2016 07:30 AM, klo uo wrote: > > Hi, > > because naive bayes is a generative model, does that mean that I can > somehow generate data based on trained model? > > For example: > > clf = BernoulliNB() > clf.fit(train, labels) > > Can I generate data for specific label? > > > Thanks, > Klo > > > _______________________________________________ > scikit-learn mailing > listscikit-learn@python.orghttps://mail.python.org/mailman/listinfo/scikit-learn > > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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