Boostrap parameter in random forest regressor?
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  The benefit of random forests comes from its creating a large variety of trees by sampling both observations and features. Bootstrap = False is telling it to sample observations with or without replacement - it should still sample when it's False, just without replacement... You tell it what share of features you want to sample by setting max_features..