For RFECV, I think that a min_features parameter could be useful. Alternatively, making XGBoost more scikit-learn compatible instead of making scikit-learn more XGBoost compatible could be another take on this.
Best, Sebastian > On Apr 30, 2017, at 3:13 PM, George Fisher <geo...@georgefisher.com> wrote: > > I found that xgboost generates an exception under RFECV when the number of > features remaining falls below 3. I fixed this for myself by adding a > 'stop_at' parameter (default=1) that stops the process in RFE when the > remaining features falls below this number. I think it might be a useful > feature more broadly than simply as a hacked work-around so I offer it as a > pull request. > > George Fisher > geo...@georgefisher.com > +1 917-514-8204 > https://github.com/grfiv > > Ubuntu 17.04 Desktop > Python 3.5.3 > IPython 6.0.0 > sklearn 0.18.1 > (xgboost 0.6) > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
