Naïve Bayes algorithms is a supervised learning algorithm for classification task based on applying Bayes’ theorem with a strong assumption that all the predictors are independent of each other. In short, the assumption states that the presence of a feature in a class is independent of any other feature in the same class. For example, a phone will be categorized as smart if it is having a touch screen, internet facility, good camera, etc. Even though all these features are dependent on each other, all these features contribute independently to find the probability that the phone is a smartphone.
In Bayesian classification, the important thing is to find the posterior probabilities defined as the probability of the class given some observed features, (Class| ).
P(Class|features)=P(Class)P(features|Class)P(features)
Here, P(Class | features) is the posterior probability of class.
P(Class) is the prior probability of class.
P(features |Class) is the likelihood which is the probability of predictor given class.
P(features) is the prior probability of predictor.
This Naïve Bayes returns the probabilities of that observation belonging to each class.
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