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foreign now that weve looked at evaluation of binary classifiers lets take a look at how the more General case of multi-class classification is handled in evaluation so in many respects multi-class evaluation is a straightforward extension of the methods we use in binary evaluation instead of two classes we have multiple classes so the results for a multi-class evaluation amount to a collection of true versus predicted binary outcomes per class and just as we saw in the binary case you can generate confusion matrices in the multi-class case theyre especially useful when you have multiple classes because there are many different kinds of errors that result from one true class being predicted as a different class so well look at an example of that classification reports that we saw in the binary case are easy to generate for the multi-class case now the one area which is uh worth a little more examination is how averaging across classes takes place there are different ways to averag