Naive Bayes classification is a supervised learning algorithm used in the field of machine learning for data classification. It is based on Bayes' theorem and assumes that all input variables are independent of each other.
In simple terms, the algorithm calculates the probability that an input belongs to each possible class, and selects the class with the highest probability as the final classification.
The "Naive" in its name refers to the assumption of independence of the input variables, which may not be true in practice. Despite this simplifying assumption, Naive Bayes classification is widely used due to its ease of implementation and its ability to handle large data sets with high dimensionality.
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