Hi, I am attempting to build some classification models where false-positives are much worse than false-negatives. Normally these two outcomes are treated equally (equal loss) in the training procedure, but I would like to be able to customize this.
I've been using the AdaBoost classifier, which works well as a general data-miner, except for this issue. I tried hacking a bit on the code by only boosting the false-positive samples, but I don't really know if that makes any sense (it tends to forget about the false-negatives). Googling around I found a paper [1] but it's not clear to me if this is what I am looking for. Thankyou for any suggestions. Simon. [1] McCane, Brendan; Novins, Kevin; Albert, Michael (2005). "Optimizing cascade classifiers.". ------------------------------------------------------------------------------ _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
