Naive Bayes Models 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.
How Naive Bayes Models Work
Despite their simplicity, Naive Bayes classifiers can be highly effective and are particularly known for their effectiveness in natural language processing tasks. They are fast and relatively uncomplicated, making them a good choice for very large datasets. The models work by calculating the probability of each class and the conditional probability of each feature belonging to each class. The class with the highest probability is then selected as the output.
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