>> (Also, I believe that GB in sklearn is unregularized in its current >> implementation?) >> > > It doesn't have a regularization term but the learning rate parameter can be > used to avoid taking overly big steps: > http://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regularization.html#example-ensemble-plot-gradient-boosting-regularization-py
Yeah. In my thesis, I boosted l1-regularized trees, and decayed the regularization parameter every time it converged. I might dig into the code and see if it would be easy to add this to sklearn. Best, Joseph ------------------------------------------------------------------------------ Everyone hates slow websites. So do we. Make your web apps faster with AppDynamics Download AppDynamics Lite for free today: http://ad.doubleclick.net/clk;258768047;13503038;j? http://info.appdynamics.com/FreeJavaPerformanceDownload.html _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
