output array([[ ][ ][ ][ ][ ][ ][ ][ ][ ][ ]]pros cons pros the followings are some pros of using naive bayes classifiersnaive bayes classification is easy to implement and fast it will converge faster than discriminative models like logistic regression it requires less training data it is highly scalable in natureor they scale linearly with the number of predictors and data points it can make probabilistic predictions and can handle continuous as well as discrete data naive bayes classification algorithm can be used for binary as well as multi-class classification problems both cons the followings are some cons of using naive bayes classifiersone of the most important cons of naive bayes classification is its strong feature independence because in real life it is almost impossible to have set of features which are completely independent of each other another issue with naive bayes classification is its 'zero frequencywhich means that if categorial variable has category but not being observed in training data setthen naive bayes model will assign zero probability to it and it will be unable to make prediction