Classifying new occurances - Multinomial Naive Bayes <p>So I have currently trained a Multinomial Naive Bayes classifier, using <code>[SKiLearn][1]</code> Now what I can do is classify test data by using predict.</p> <p>But if I want to run this every night, as a script, I clearly need to always have a classifier already trained up! Now what I'd like to be able to do, is take classifier coefficients, informative words, and use these to classify new data. </p> <p>Is this possible - to develop my own method for classification? Or should I be simply training the SkiLearn classifier nightly?</p> <p>EDIT: One thing, it seems I can do, is retain and <a href="http://stackoverflow.com/questions/10592605/save-naivebayes-classifier-to-disk-in-scikits-learn">save my trained classifier</a>. </p> <p>However with logistic regression, you can take the coefficients and use these on new data. Is there anything similar to this for NB?</p>