Naive Bayes Classifiers

Naive Bayes classifiers are a family of algorithms based on Bayes' Theorem, widely used for classification tasks despite the assumption of feature independence. These classifiers are popular for their simplicity and efficiency, especially in text classification tasks like spam filtering and sentiment detection. Naive Bayes is a probabilistic classifier, which predicts the probability of an instance belonging to a class given a set of feature values. It is known for its speed and ability to handle high-dimensional data. This article covers the theory, implementation, and practical applications of Naive Bayes classifiers.

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