Desktop Survival Guide
by Graham Williams

Bayes Classifier:

$\mathcal{P}(\mathit{Moulin Rouge}\vert\ldots) \approx$

Bayes classifiers came in two varieties: naïve and full. Naïve Bayes is a technique for estimating probabilities of individual variable values, given a class, from training data and to then allow the use of these probabilities to classify new entities. Naïve Bayes has been demonstrated usefully on moderate and large datasets. It can be used for diagnosis and classification tasks. Despite the fact that the assumption of conditional independence is often violated the approach continues to work well. Full Bayes ...


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