# how to retrain model with periodic new features?

I've trained a gradient boosting classification model. But, suppose i've a set of fixed features F1,F2....Fn and new features which are added weekly (no. of actions done in that week). So, after 2 weeks dataset to be trained on is :

   Fixed           Dynamic
F1 ,F2 .....Fn    W1 ,W2

After 3 weeks

Fixed           Dynamic
F1 ,F2 .....Fn    W1 ,W2, W3


How do we approach this problem on production server, is there any approach available which allow model to be retrained on new features and not only on new observations ?

• Pls elaborate about these new features w1, w2, ... that become available over time. I suspect they're not really indep features that require a new column in your design matrix. Mar 3 '18 at 19:29