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In evaluating multi-class classification, the process is an extension of binary classification methods, involving multiple classes instead of just two. The results consist of true versus predicted outcomes for each class. Confusion matrices remain valuable in this context, helping to identify various errors stemming from misclassifications. While classification reports akin to those in binary evaluation can be easily generated, a key aspect needing further attention is the averaging of metrics across classes, as there are different methods to accomplish this.