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 ?
The way gradient boosting is constructed and trained, there is not an obvious solution for this without just training from scratch. Other models might be more suitable for this (Adding this to a neural network and retraining this will take less time than from scratch I think). Another approach would be to use these time-based lag features as a time series, that way you just input your features and it will learn relationships between the past few weeks instead.