Updating a fitted sklearn Random Forest Model

Question

While running in production, is it possible to update a trained model with new data without re-fitting the model? I see you can use the warm_start parameter to enable adding trees to the model; however, I am looking for a way to update the existing trees with the incoming data.

Answer 1

As far as I can tell, this is not possible with sklearn (as they seem to implement the classical Breiman algorithm). However, you might have a look at Mondrian Forests (https://papers.nips.cc/paper/5234-mondrian-forests-efficient-online-random-forests.pdf, python implementation: https://github.com/balajiln/mondrianforest).