Thanks to the amazing course I am able to train a model on multi-class text classification, now how can I generate the Confusion matrix and roc_auc curve for my model. I have tried using evaluate library but there is no option for confusion matrix in evaluate library and roc_auc module of evaluate.list_evaluation_modules() is not giving value for each epoch so I can not plot the graph using seaborn. So anyone can please share the code or the resource so that I can implement them, thanks in advance.
ROC curve summarizes the performance by combining confusion matrices at all threshold values . AUC turns the ROC curve into a numeric representation of performance for a binary space bar clicker classifier. AUC is the The ROC curve **shows the trade-off between sensitivity (or TPR) and specificity (1 – FPR). Classifiers that give curves closer to the top-left corner indicate a better performance. As a baseline, a random classifier is expected to give points lying along the diagonal (