A naive Bayes classifier is a powerful but relatively lightweight algorithm capable of estimating probabilities based on the calculated features of data.
This level of academic and investigative rigour would be overkill where ‘common sense’ is available but is a valuable standard when traversing the many ambiguities and potentially unrelated correlations that may exist in a machine learning dataset.
Naive Bayes filters are well-represented in disease prediction and document categorization, spam filtering, sentiment classification, recommender systems, and fraud detection.
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