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

2017-04-20 Thread Olga Lyashevska
Thank you. It seems that information value can only be calculated for a binary classification dataset, however my response variable is continuous. On 20/04/17 05:51, urvesh patel wrote: I believe your random variable by chance have some predictive power. In R, use Information package and chec

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

2017-04-19 Thread urvesh patel
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

[scikit-learn] feature importance calculation in gradient boosting

2017-04-18 Thread Olga Lyashevska
Hi, I would like to understand how feature importances are calculated in gradient boosting regression. I know that these are the relevant functions: https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/gradient_boosting.py#L1165 https://github.com/scikit-learn/scikit-learn