Naïve Bayes is a classification technique that serves as the basis for implementing several classifier modeling algorithms. Naïve Bayes-based classifiers are considered some of the simplest, fastest, and easiest-to-use machine learning techniques, yet are still effective for real-world applications.
Naïve Bayes is based on Bayes' theorem, formulated by 18th-century statistician Thomas Bayes. This theorem assesses the probability that an event will occur based on conditions related to the event. For example, an individual with Parkinson's disease typically has voice variations; hence such symptoms are considered related to the prediction of a Parkinson's diagnosis. The original Bayes' theorem provides a method to determine the probability of a target event, and the Naïve variant extends and simplifies this method.
This code and project will be of interest to any Python (or even other) programmers wanting to go to the next level in terms of building machine learning predictability into their applications.
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Naïve Bayes is a classification technique that serves as the basis for implementing several classifier modeling algorithms. Naïve Bayes-based classifiers are considered some of the simplest, fastest, and easiest-to-use machine learning techniques, yet are still effective for real-world applications.
