classification algorithmsmachine naive bayes introduction to naive bayes algorithm naive bayes algorithms is classification technique based on applying bayestheorem with strong assumption that all the predictors are independent to each other in simple wordsthe assumption is that the presence of feature in class is independent to the presence of any other feature in the same class for examplea phone may be considered as smart if it is having touch screeninternet facilitygood camera etc though all these features are dependent on each otherthey contribute independently to the probability of that the phone is smart phone in bayesian classificationthe main interest is to find the posterior probabilities the probability of label given some observed featuresp( featureswith the help of bayes theoremwe can express this in quantitative form as followsp( featuresp( ) (features lp(featuresherep( featuresis the posterior probability of class (lis the prior probability of class (features lis the likelihood which is the probability of predictor given class (featuresis the prior probability of predictor building model using naive bayes in python python libraryscikit learn is the most useful library that helps us to build naive bayes model in python we have the following three types of naive bayes model under scikit learn python librarygaussian naive bayes it is the simplest naive bayes classifier having the assumption that the data from each label is drawn from simple gaussian distribution multinomial naive bayes another useful naive bayes classifier is multinomial naive bayes in which the features are assumed to be drawn from simple multinomial distribution such kind of naive bayes are most appropriate for the features that represents discrete counts bernoulli naive bayes another important model is bernoulli naive bayes in which features are assumed to be binary ( and stext classification with 'bag of wordsmodel can be an application of bernoulli naive bayes