Hello J.B,
I could simply create some ROC curves as shown in the scikit-learn documentation by selecting only 2 classes and then repeating by selecting other pair of classes (in total I have 3 classes so this would result in 3 different ROC figures). An alternative would be I would like to plot the mean and confidence intervals of the 3-class Cohen Kappa metric as estimated by KFolds (k=5) cross-validation. Any tips about this ? Cheers, Makis On 21 Jul 2018, at 16:02, Brown J.B. via scikit-learn <scikit-learn@python.org<mailto:scikit-learn@python.org>> wrote: Hello Makis, 2018-07-20 23:44 GMT+09:00 Andreas Mueller <t3k...@gmail.com<mailto:t3k...@gmail.com>>: There is no single roc curve for a 3 class problem. So what do you want to plot? On 07/20/2018 10:40 AM, serafim loukas wrote: What I want to do is to plot the average(mean) ROC across Folds for a 3-class case. The prototypical ROC curve uses True Positive Rate and False Positive Rate for its axes, so it is for 2-class problems, and not for 3+-class problems, as Andy mentioned. Perhaps you are wanting the mean and confidence intervals of the n-class Cohen Kappa metric as estimated by either many folds of cross validation, or you want to evaluate your classifier by repeated subsampling experiments and Kappa value distribution/histogram? Hope this helps, J.B. _______________________________________________ scikit-learn mailing list scikit-learn@python.org<mailto:scikit-learn@python.org> https://mail.python.org/mailman/listinfo/scikit-learn
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