Hi Egor,

River has implemented AdaBoost. I think it depends on the data and application. If you have "enough" data and don't expect the relationship between input and output data to change much or change quickly, then a slightly stale batch-trained XGBoost algorithm could be an acceptable solution. In cases where you don't have much data and want a model to learn as you go, or where the relationship between input and output data to change, continual learning might be more appropriate.

Data scientist working in the financial services industry