Can I fine tune the xgboost model instead of re-training it? I am using the xgboost library. My system runs a cronjob each night, where it pulls the data from the database and trains the model. However, I would like to remove the re-training of the model again and again, and just fine-tune it with any new data that came in the database. In sklearn's implemantation (http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html) one could use warm_start option, what about xgboost ?
I see that in the current version of python wrapper of xgboost you can specify file name or existing xgboost model (class Booster) in train function.