Performance Metrics
The performance metrics in the case of multiclass classification would be the same as what you used for binary classification in the previous chapter, that is, precision, recall, and F1 score, obtained using a confusion matrix.
In the case of a multiclass classification problem, you average out the metrics to find the micro-average or macro-average of precision, recall, and F1 score in a k-class system, where k is the number of classes. Averaging is useful in the case of multiclass classification since you have multiple class labels. This is because each classifier is going to give one class as the prediction; however, in the end, you are just looking for one class. In such cases, an aggregation such as averaging helps in getting the final output.
The macro-average computes the metrics such as precision (PRE), recall (Recall), or F1 score (F1) of each class independently and takes the average (all the classes are treated equally):
Figure 9.4: The macro...
