On a related note, here is an implementeation of Logistic Regression applied to one-hot features obtained from leaf membership info of a GBRT model:
http://nbviewer.ipython.org/github/ogrisel/notebooks/blob/master/sklearn_demos/Income%20classification.ipynb#Using-the-boosted-trees-to-extract-features-for-a-Logistic-Regression-model This is inspired by this paper from Facebook: https://www.facebook.com/publications/329190253909587/ . It's easy to implement and seems to work quite well. -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ Slashdot TV. Video for Nerds. Stuff that Matters. http://pubads.g.doubleclick.net/gampad/clk?id=160591471&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
