Timeline for AUC and class imbalance in training/test dataset
Current License: CC BY-SA 3.0
7 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Feb 6, 2017 at 17:49 | vote | accept | Munichong | ||
| Feb 6, 2017 at 16:03 | comment | added | David Ernst | Roc is based on multiple confusion matrices based on different cutoffs. Read more theory till you understand what this means. I'm not familiar with scikit learn and its syntax. No the sum of two aucs with regard to both classes doesn't need to be 1. | |
| Feb 6, 2017 at 16:02 | comment | added | Munichong | Trying different inputs, I also find that the sum of the two results is always 1. Am I right? | |
| Feb 6, 2017 at 15:57 | comment | added | Munichong | Thanks. I tried some experiments. But I get confused on calculating AUC for class 0: y_true=[1,0], y_pred=[0.9, 0.8], I use the sklearn.metrics.auc function to compute AUC. The result is 1.0. I assume it only reflects how the classifier identifies class1. I then switch 1 and 0 in y_true: y_true=[0,1], y_pred=[0.9, 0.8], The result is 0.0. This is how the classifier identifies class0. Am I right? | |
| Feb 6, 2017 at 14:12 | comment | added | David Ernst | That auc would look at the positive class. Nothing prevents you from computing and with respect to both classes and averaging it. But most of the time, your application scenario makes it clear which one is the positive class. | |
| Feb 6, 2017 at 14:01 | comment | added | Munichong | In binary classification, do we usually distinguish "AUC for positive class" and "AUC for negative class"? OR there is only one AUC? I think it depends on whether "default" AUC calculated by TPR and NPR can reflect the ability of identifying both positive and negative classes OR only positive class. | |
| Feb 6, 2017 at 13:31 | history | answered | David Ernst | CC BY-SA 3.0 |