Naive Bayes is a supervised machine learning algorithm used to classify objects based on their features. This algorithm uses Bayes$$ theorem, which states that the probability of an event occurring given certain conditions or evidence is proportional to the probability of the same event occurring without that particular evidence.

Naive Bayes makes predictions by taking into account all the features in the dataset at once and then making a prediction about the likelihood of an instance belonging to a particular class. This algorithm tends to work well for classification tasks with limited data and is commonly used in text classification tasks such as spam filtering.

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