Every example I come across any kind of iterative learning on Random Forest/XGBoost/LightGBM, it just continuously grows the number of estimators for new batches of data by
num_boost_rounds every time
.fit() gets applied [...]. Most of them seem to rely on iterative learning for training on very large datasets that can't be loaded into memory at once.
However, I want to implement a continuous learning pipeline (with LightGBM; Python) that takes newly available data on a daily basis in order to update an existing model (without the need to retrain on the whole [growing] dataset; stateful). The initially mentioned approach would imply that my model will ever increase its tree count.
Is it possible to train tree-based algorithms so that the estimators (split thresholds) themselves get updated/adjusted in contrast to only adding estimators?