Naive Bayes Classifiers

Naive Bayes classifiers are a family of probabilistic algorithms used for classification tasks in machine learning and data science. They are based on Bayes' theorem, which describes the probability of an event based on prior knowledge of conditions that might be related to the event. Naive Bayes classifiers assume that the presence or absence of a particular feature is independent of the presence or absence of any other feature, hence the term 'naive'. Despite this simplifying assumption, Naive Bayes classifiers have been shown to be effective in many real-world applications, particularly in natural language processing and text classification. They are relatively simple to implement and require a small amount of training data to estimate the necessary parameters. Naive Bayes classifiers are also known for their ability to handle high-dimensional data with a large number of features, making them a popular choice for tasks such as spam filtering and sentiment analysis.

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Naive Bayes Classifiers

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