Just a comment from the statistics sidelines taking log of target and fitting a linear or other model doesn't make it into a Poisson model.
But maybe "Poisson loss" in machine learning is unrelated to the Poisson distribution or a Poisson model with E(y| x) = exp(x beta). ? Josef On Tue, Jul 28, 2015 at 2:46 PM, Andreas Mueller <t3k...@gmail.com> wrote: > I'd be happy with adding Poisson loss to more models, thought I think it > would be more natural to first add it to GLM before GBM ;) > If the addition is straight-forward, I think it would be a nice > contribution nevertheless. > 1) for the user to do np.exp(gbmpoisson.predict(X)) is not acceptable. > This needs to be automatic. It would be best if this could be done in a > minimally intrusive way. > > 2) I'm not sure, maybe Peter can comment? > > 3) I would rather contribute sooner, but other might thing differently. > Silently ignoring sample weights is not an option, but you can error if > they are provided. > > Hth, > Andy > > > On 07/23/2015 08:52 PM, Peter Rickwood wrote: > > > Hello sklearn developers, > > I'd like the GBM implementation in sklearn to support Poisson loss, and > I'm comfortable in writing the code (I have modified my local sklearn > source already and am using Poisson loss GBM's). > > The sklearn site says to get in touch via this list before making a > contribution, so is it worth me to submitting something along these lines? > > If the answer is yes, some quick questions: > > 1) The simplest implementation of poisson loss GBMs is to work in > log-space (i.e. the GBM predicts log(target) rather than target), and > require the user to then take the exponential of those predictions. So, you > would need to do something like: > gbmpoisson = sklearn.ensemble.GradientBoostingRegressor(...) > gbmpoisson.fit(X,y) > preds = np.exp(predict(X)) > I am comfortable making changes to the source for this to work, but I'm > not comfortable changing any of the higher-level interface to deal > automatically with the transform. In other words, other developers would > need to either be OK with the GBM returning transformed predictions in the > case where "poisson" loss is chosen, or would need to change code in the > 'predict' function to automatically do the transformation is poisson loss > was specified. Is this OK? > > 2) If I do contribute, can you advise what the best tests are to > test/validate GBM loss functions before they are considered to 'work'? > > 3) Allowing for weighted samples is in theory easy enough to implement, > but is not something I have implemented yet. Is it better to contribute > code sooner that doesn't handle weighting (i.e. just ignores sample > weights), or later that does? > > > > > Cheers, and thanks for all your work on sklearn. Fantastic tool/library, > > > > Peter > > > > > > > > ------------------------------------------------------------------------------ > > > > _______________________________________________ > Scikit-learn-general mailing > listScikit-learn-general@lists.sourceforge.nethttps://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 > >
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