What is online bagging and how does it work?

Online bagging adapts bagging to streaming data by incrementally updating models.

Online bagging simulates bootstrap sampling in streaming environments using Poisson-distributed weights for incoming samples. Each base learner is updated incrementally using partial_fit methods. This allows ensembles to continuously learn from data streams without retraining from scratch. It is widely used in real-time analytics systems where data arrives continuously.