Bagging or Random Subspace Method for Random Forest? I am reading a lot about the Random Forest regressor. I read about bagging (Bootstrap and aggregation) and random subspace. But I am not sure if the random forest regressor is just using bagging or bagging and random subspace method. Because in some articles, the random subspace method seems like an alternative to the bagging method.
Most modern implementations do both, at least optionally.

sklearn has max_features and bootstrap.
ranger has mtry and replace/sample.fraction.
xgboost's random forest has colsample_bynode and subsample.
h2o has mtries/col_sample_rate_per_tree and sample_rate (and a couple modifiers).