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 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
<o.lyashevsk...@gmail.com <mailto:o.lyashevsk...@gmail.com>> wrote:
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/blob/fc2f24927fc37d7e42917369f17de045b14c59b5/sklearn/tree/_tree.pyx#L1056
From the literature and elsewhere I understand that Gini impurity is
calculated. What is this exactly and how does it relate to 'gain' vs
'frequency' implemented in XGBoost?
http://xgboost.readthedocs.io/en/latest/R-package/discoverYourData.html
My problem is that when I fit exactly same model in sklearn and gbm (R
package) I get different variable importance plots. One of the variables
which was generated randomly (keeping all other variables real) appears
to be very important in sklearn and very unimportant in gbm. How is this
possible that completely random variable gets the highest importance?
Many thanks,
Olga
_______________________________________________
scikit-learn mailing list
scikit-learn@python.org <mailto: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
_______________________________________________
scikit-learn mailing list
scikit-learn@python.org
https://mail.python.org/mailman/listinfo/scikit-learn
