Re: [scikit-learn] feature importance calculation in gradient boosting

I believe your random variable by chance have some predictive power. In R, use Information package and check information value of that randomly created variable. If it is > 0.05 then it has good predictive power. On Tue, Apr 18, 2017 at 7:47 AM Olga Lyashevska wrote: > Hi, > > I would like to und

Re: [scikit-learn] XGboost Classifier error

Hi Olivier, Thanks for your info.I will follow it from now on. Details of traceback are given below: --Full traceback--- Fitting 3 folds for each of 10 candidates, totalling 30 fits C:\Users\ssampathkumar\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\grid_sear

Re: [scikit-learn] XGboost Classifier error

Please provide the full traceback. Without it it's impossible to tell whether the problem is in scikit-learn or xgboost. Also, please provide a minimal reproduction script as explained in: http://scikit-learn.org/stable/faq.html#what-s-the-best-way-to-get-help-on-scikit-learn-usage -- Olivier _