> wondering what changes are needed to make > RandomForestClassifier competitive with xgboost and H20 at
Do you mean in terms of predictive performance (not computational efficiency)? Not sure what other's think, but I wouldn't change the core algorithm since otherwise it's not really a "Random forest" anymore as it is described in literature -- and that would be very confusing for users and researchers. > On Mar 22, 2016, at 7:52 AM, Raphael C <drr...@gmail.com> wrote: > >> >> - In tree-based Not handling categorical variables as such hurts us a lot >> There's a PR to fix that, it still needs a bit of love: >> https://github.com/scikit-learn/scikit-learn/pull/4899 >> > > This is a conversation moved from > https://github.com/scikit-learn/scikit-learn/pull/4899 . > > In the light of the comment above and comments in the PR, I was > wondering what changes are needed to make > RandomForestClassifier competitive with xgboost and H20 at > http://datascience.la/benchmarking-random-forest-implementations/ . > > Raphael > > ------------------------------------------------------------------------------ > Transform Data into Opportunity. > Accelerate data analysis in your applications with > Intel Data Analytics Acceleration Library. > Click to learn more. > http://pubads.g.doubleclick.net/gampad/clk?id=278785351&iu=/4140 > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ Transform Data into Opportunity. Accelerate data analysis in your applications with Intel Data Analytics Acceleration Library. Click to learn more. http://pubads.g.doubleclick.net/gampad/clk?id=278785351&iu=/4140 _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
