Hi Simon. In general in scikit-learn you could use class-weights to make one class more important then the other. Unfortunately that is not implemented for AdaBoost yet. You can however use the sample_weights parameter of the fit method, and create sample weights either by hand based on the class, or use the compute_sample_weights function: https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/class_weight.py#L82
The other possibility is to simply threshold the predict_proba of your classifier differently based on your cost. Best, Andy On 08/04/2015 10:29 AM, Simon Burton wrote: > 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 ------------------------------------------------------------------------------ _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
