First of all many thanks for the very nice repository and clean code!
I was wondering whether this method would be applicable to non-binary classification tasks. By looking at the code, I imagine it could make sense if the class is an ordinal variable, but in the general case the mean over the predicted classes would not be meaningful.
The original paper does not discuss this, and it seems to me the extension to multi-class problems is not super trivial. However, as your code imposes no restrictions on the number of classes, I was wondering if you have considered this problem, and whether you know of a good workaround. Would running the code on the predicted probability of each class make sense? That is, computing the variance of the output of predict_proba instead of predict.
First of all many thanks for the very nice repository and clean code!
I was wondering whether this method would be applicable to non-binary classification tasks. By looking at the code, I imagine it could make sense if the class is an ordinal variable, but in the general case the mean over the predicted classes would not be meaningful.
The original paper does not discuss this, and it seems to me the extension to multi-class problems is not super trivial. However, as your code imposes no restrictions on the number of classes, I was wondering if you have considered this problem, and whether you know of a good workaround. Would running the code on the predicted probability of each class make sense? That is, computing the variance of the output of
predict_probainstead ofpredict.Many thanks!