Naive Bayes methods are a set of supervised learning algorithms based on applying Bayes’ theorem with the “naive” assumption of conditional independence between every pair of features given the value of the class variable. Naïve Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem. Maximum-likelihood training can be done by evaluating a closed form expression which takes linear time, rather than by expensive iterative approximation as used for many other types of classifiers. To implement- ‘from sklearn.naive_bayes import GaussianNB’
The datasets used in this study are available at - https://data.matr.io/1