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 ?