The naive Bayes classifier combines this model with a decision rule. One common rule is to pick the hypothesis that is most probable; this is known as the maximum a posteriori or MAP decision rule. The corresponding classifier, a Bayes classifier, is the function that assigns a
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Inquire NowIn the below example I implemented a Naive Bayes classifier in python and in the following I used sklearn package to solve it again: and the output is: [crayon5e188a90cc197652555153/] [crayon5e188a90cc1aa305343768/]
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Inquire NowAn advantage of the naive Bayes classifier is that it only requires a small amount of training data to estimate the parameters means and variances of the variables necessary for classification. Because independent variables are assumed, only the variances of the variables for each class need to be determined and not the entire covariance matrix .
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