Re:-AUC,ROC,Precision,Recall
How to make the charts for AUC, ROC, Precision, Recall for the multi-classification problem? Which operator do I need to use it?
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Hello @guptasha
To get an AUC, you need to have a ROC curve. ROC curve is plotted for Binary classification. In the case of multiple class classification, you can get multiple ROC curves when you do one vs all classes type classification. This can be done in rapidminer using Polynomial to Binomial operator. Once you get AUC's for all classes, then you can average them for final AUC of Multiclass.
Recall and Precision can be found directly with multi-classification, using performance (classification) operator. In this operator, you have weighted mean recall and weighted mean precision options to choose.
Similar discussion here
https://community.rapidminer.com/discussion/55258/specificity-sensitivity-and-auc-measures-via-rm-v9-1#latest
Thanks
To get an AUC, you need to have a ROC curve. ROC curve is plotted for Binary classification. In the case of multiple class classification, you can get multiple ROC curves when you do one vs all classes type classification. This can be done in rapidminer using Polynomial to Binomial operator. Once you get AUC's for all classes, then you can average them for final AUC of Multiclass.
Recall and Precision can be found directly with multi-classification, using performance (classification) operator. In this operator, you have weighted mean recall and weighted mean precision options to choose.
Similar discussion here
https://community.rapidminer.com/discussion/55258/specificity-sensitivity-and-auc-measures-via-rm-v9-1#latest
Thanks
To get an AUC, you need to have a ROC curve. ROC curve is plotted for Binary classification. In the case of multiple class classification, you can get multiple ROC curves when you do one vs all classes type classification. This can be done in rapidminer using Polynomial to Binomial operator. Once you get AUC's for all classes, then you can average them for final AUC of Multiclass.
Recall and Precision can be found directly with multi-classification, using performance (classification) operator. In this operator, you have weighted mean recall and weighted mean precision options to choose.
Similar discussion here
https://community.rapidminer.com/discussion/55258/specificity-sensitivity-and-auc-measures-via-rm-v9-1#latest
Thanks