monkeysingmonkeynew
monkeysingmonkeynew OP t1_j4un2xm wrote
Reply to comment by __lawless in [D] Is it possible to update random forest parameters with new data instead of retraining on all data? by monkeysingmonkeynew
OK I can almost see this working, thanks for the suggestion. The only thing that would prevent me from implementing this solution is that by taking the sum of the two models, it would let m_1 give as equal a contribution to the result as m_1. However I expect a single days data to be noisy, Thus I would need the contribution of the new days data to be down weighted somehow.
monkeysingmonkeynew OP t1_j4r6lwj wrote
Reply to comment by __lawless in [D] Is it possible to update random forest parameters with new data instead of retraining on all data? by monkeysingmonkeynew
this sounds pretty cool. but I don't follow every step. By "calculate the errors" do you mean for example, extract the predicted probabilities from the actual outcome?
Also, I didn't get your last part about inference, what exactly are you referring to there?
monkeysingmonkeynew OP t1_j4r539v wrote
Reply to comment by BenoitParis in [D] Is it possible to update random forest parameters with new data instead of retraining on all data? by monkeysingmonkeynew
Yes, it's ok if i run it once a day, but I need to backtest two years of data and so it's not feasible on a laptop, or affordable on a GPU
monkeysingmonkeynew OP t1_j4pjoxj wrote
Reply to comment by thiru_2718 in [D] Is it possible to update random forest parameters with new data instead of retraining on all data? by monkeysingmonkeynew
Thanks! I'll muse this over
monkeysingmonkeynew OP t1_j4z1fum wrote
Reply to comment by Equivalent-Way3 in [D] Is it possible to update random forest parameters with new data instead of retraining on all data? by monkeysingmonkeynew
Thanks! Do you have any more info on how to do it with XGBoost?