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Can anyone explain what the Bayes error rate is in Naive Bayes, for instance in matlab:

[class, err]  = classify(UnseenTestdata, training_data, target_class,'diaglinear')

I get an error rate (err) of 0.5040 what does this mean? Im not a math orientated person so plain english would help. Is this good, bad or very ugly?

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From the matlab help function:

Err is an estimate of the misclassification error rate that is based on the training data. CLASSIFY returns the apparent error rate, i.e., the percentage of observations in the TRAINING that are misclassified, weighted by the prior probabilities for the groups.

So, if 0.5 is close to your guessing rate you are doing poorly, otherwise it's not bad.

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How can you say your doing poorly but then say otherwise its not bad? At 50% accuracy across all classes its kinda rubbish. I had been thrown off because my accuracy from the confusion matrix was 81.49% but I had noticed that it classified the biggest body from the testdata correct (one class) while leaving alot of the other classes with almost 0% accuracy. – Garrith Graham Nov 19 '12 at 16:28
Not sure if i understand correctly, but as I read it you have the output of two methods. In this case you can just compare the results manually and determine which one is better. – Dennis Jaheruddin Nov 19 '12 at 16:41

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