Naive Bayes

Naive Bayes is an easy mastering algorithm that makes use of Bayes rule together with a sturdy assumption that the attributes are conditionally impartial, given the magnificence. While this independence assumption is regularly violated in practice, naïve Bayes although often promises aggressive classification accuracy. Coupled with its computational efficiency and many different applicable features, these results in naïve Bayes being broadly applied in exercise. Naïve Bayes algorithm is a supervised learning set of rules, that's based on Bayes theorem and used for fixing type problems. It is specially utilized in text classification that includes an excessive-dimensional education dataset.

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