any continuous datawhile bernoullinb assumes binary data and multinomialnb assumes count data (that isthat each feature represents an integer count of somethinglike how often word appears in sentencebernoullinb and multinomialnb are mostly used in text data classification the bernoullinb classifier counts how often every feature of each class is not zero this is most easily understood with an examplein[ ] np array([[ ][ ][ ][ ]] np array([ ]herewe have four data pointswith four binary features each there are two classes and for class (the first and third data points)the first feature is zero two times and nonzero zero timesthe second feature is zero one time and nonzero one timeand so on these same counts are then calculated for the data points in the second class counting the nonzero entries per class in essence looks like thisin[ ]counts {for label in np unique( )iterate over each class count (sumentries of per feature counts[labelx[ =labelsum(axis= print("feature counts:\ {}format(counts)out[ ]feature counts{ array([ ]) array([ ])the other two naive bayes modelsmultinomialnb and gaussiannbare slightly different in what kinds of statistics they compute multinomialnb takes into account the average value of each feature for each classwhile gaussiannb stores the average value as well as the standard deviation of each feature for each class to make predictiona data point is compared to the statistics for each of the classesand the best matching class is predicted interestinglyfor both multinomialnb and bernoullinbthis leads to prediction formula that is of the same form as in the linear models (see "linear models for classificationon page unfortunatelycoef_ for the naive bayes models has somewhat different meaning than in the linear modelsin that coef_ is not the same as supervised machine learning algorithms