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Despite its practical applications, particularly in text mining, Naive Bayes is labelled "Naive" because it relies on an assumption that is nearly impossible to verify in real-world data: the conditional probability is calculated as the pure product of the individual probabilities of components. This necessitates complete feature independence, which is a criterion that is unlikely to be realised in real life.

Simply defined, machine learning allows a user to submit massive amounts of data to a computer algorithm, which then analyses and makes data-driven suggestions and decisions based only on the supplied data.

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