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Robust Naive Bayes

Robust Naive Bayes extends the standard Naive Bayes classifier to handle uncertainty or noise in class-conditional probability estimates by replacing point probability estimates with intervals or sets of distributions. The canonical formulation — the Naive Credal Classifier proposed by Zaffalon (2002) — uses imprecise-probability sets so that predictions are made only when all distributions in the set agree, withholding a label when evidence is insufficient.

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Robust Naive Bayes Classifier

Rekodi ya mbinu ya kiajenda · ml-model / machine-learning

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