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Naive Bayes

Naive Bayes

Naive Bayes is a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features. Despite the simplifying assumption that features are independent given the class label, Naive Bayes classifiers often perform well in practice, especially for text classification tasks such as spam detection and sentiment analysis. The model calculates the posterior probability of each class given the observed features and assigns the class with the highest probability to the instance. Naive Bayes classifiers are fast, scalable, and easy to implement, making them suitable for large datasets and real-time applications. Their simplicity and effectiveness make them a popular choice for initial baseline models in classification problems.

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