I trained models based on the same dataset, using random forest (sklearn) and CatBoost.
I use n_estimators=1000 for random forest, and n_estimators(iterations)=1000 for CatBoost. The random forest has significantly larger model size compared to that of CatBoost.
They both have a lot of trees in the model. Why the size difference is so large?
Without seeing your code is hard to answer. That said, base-learners in a random forest (i.e. the trees) usually are not confined to a small tree depth. The most common setting is to confine the tree depth by the minimum number of samples per tree or just prune the tree based on impurity decreases (e.g. like the RPART doc suggests). On the contrary, gradient boosting machine methodologies usually, actively discourage very deep trees to avoid over-fitting (e.g. Catboost doc suggest that the optimal depth range are from 4 to 10). As such, we might have the same number of trees/base-learner in both methods but the random forest will probably have "chunkier" base-learners leading to an overall larger model size.
