2011/12/6 James Bergstra <[email protected]>: > On Fri, Dec 2, 2011 at 12:54 PM, Peter Prettenhofer > <[email protected]> wrote: >> [...] >> > > How does the current tree implementation support boosting? I don't see > anything in the code about weighted samples. > > - James
You're right - we don't support sample weights at the moment but one might use sampling with replacement to implement e.g. AdaBoost. Gradient boosting [1], on the other hand, does not need sample weights but fits a series of regression trees on the residuals of their predecessors. You can think of gradient boosting as a generalization of boosting (forward stage-wise additive modelling) for arbitrary loss functions (e.g. if you use exponential loss you recover AdaBoost) [1] http://en.wikipedia.org/wiki/Gradient_boosting best, Peter > > ------------------------------------------------------------------------------ > Cloud Services Checklist: Pricing and Packaging Optimization > This white paper is intended to serve as a reference, checklist and point of > discussion for anyone considering optimizing the pricing and packaging model > of a cloud services business. Read Now! > http://www.accelacomm.com/jaw/sfnl/114/51491232/ > _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general -- Peter Prettenhofer ------------------------------------------------------------------------------ Cloud Services Checklist: Pricing and Packaging Optimization This white paper is intended to serve as a reference, checklist and point of discussion for anyone considering optimizing the pricing and packaging model of a cloud services business. Read Now! http://www.accelacomm.com/jaw/sfnl/114/51491232/ _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
