Dear Scikit Team, I am working to build a recommendation model on streaming data. Algorithm used: BIRCH # trained with 1000 records birch_model.partial_fit(x) label count is 1000 and cluster count is 4 joblib_file = filename joblib.dump(birch_model, joblib_file) joblib_model = joblib.load(joblib_file) # trained with 100 new records records joblib_model .partial_fit(x) label count is 100 and cluster count is 3 Is it a correct way to apply on streaming data? I could not find any documentation which explains that "when we retrain the model with a new set of records it also carries previous information". Thanks & Regards, Shukla Sharma