Is there any classifer that can natively support unbalanced datasets? Or what best practices you can suggest to handle such datasets?
For example I want to solve task called "pedestrian detection" classical approach use linear SVM, but it can't handle unbalanced dataset (lots of background examples, small number of positive examples - people).Maybe there is something better than SVM? (I already know about undersampling/oversampling and weighted SVM).
It would be great if in answer you link to some scikit-learn classification algorithm.