What is Naive Bayes?
Naive Bayes is a family of probabilistic algorithms based on applying Bayes' theorem with the “naive” assumption of independence between every pair of features. Despite its simplicity, Naive Bayes can be surprisingly effective for many classification tasks, particularly in natural language processing.
Applications of Naive Bayes in NLP
Text classification: Naive Bayes can be used for tasks such as sentiment analysis or topic classification, where the goal is to assign a label to a given text based on its content.
Spam filtering: Naive Bayes is widely used in email spam filtering, where the algorithm classifies emails as spam or not spam based on the words and phrases they contain.
Language detection: Naive Bayes can be employed to identify the language of a given text by analyzing the frequencies of character n-grams.
Naive Bayes Algorithm - An article on the basics of the Naive Bayes algorithm.
What is Naive Bayes Classifier - IBM’s resource on Naive Bayes classifier, its principles, and uses.
Naive Bayes Classifier explained - A comprehensive guide to understanding, building, and implementing Naive Bayes classifiers.
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