When you have a multiclass classification problem, what is the right way to evaluate it's performance?
What I usually do is to display the confusion matrix and the
classification_report() offered by the scikit-learn python library.
However I wonder why nobody ever calculates the Precision vs. Recall and the ROC curves. Should they be calculated as well?
I found the following example which calculates them but when I try to reverse-engineer the problem to calculate for example the precision and recall of each class I do not get the same results as the ones from the
classification_report() (as you can see here).
QUESTION: Would you be able to provide an example to have a full analysis of the performance of a multiclass classification problem?