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TNaiveBayes Class

Implementation of the "Naive Bayes" classification algorithm.

TNaiveBayes = class(TStatisticClassifier);

The classifier uses the Laplace estimate of the prior probabilities and m-estimate of the probability. Naive Bayes is still the most succesfull classifier for "statistics":"single example" classification case. It outperformes neural networks and machine learning algorithms in most of the real life cases, despite its inability to detect and account for non-linearities present in the learn datasets. This does not mean that it is not sensitive to presence of non-linearities, but the occurence of such non-linearities which could significantly affect the classification accuracy in the real world test databases is very rare. The drawback of Naive Bayes is its inability to process real values natively. All real valued attributes have to be converted to a discrete representation. This requires, that the entire "knowledge" database is known in advance.

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