# 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 ?

• Are you using xgboost bound to Python? The command-line version definitely does what you want: model_in [default=NULL] path to input model, needed for test, eval, dump, if it is specified in training, xgboost will continue training from the input model - but I don't know whether and how the Python bindings support this option. – Neil Slater Sep 18 '15 at 12:25
• yes I am using the python version, I will check if something like this is supported. – trailblazer Sep 18 '15 at 23:30
• It's not supported in the python module, but there is a fork that shows the simple change needed to enable this. github.com/Far0n/xgboost/commit/… – inversion Sep 19 '15 at 14:36