Boostrap parameter in random forest regressor? There's one parameter in RandomForestRegressor which is bootstrap. By default
bootstrap=True


  bootstrap : boolean, optional (default=True)
  
  Whether bootstrap samples are used when building trees.


So from the docs if I set bootstrap=False then I guess bootstrap samples are not used but I'm really confused on what is bootstrap samples mean here?

There were explanations but it's really confusing. Can someone please explain it in a simpler term? And also does bootstrap=True help in improving the model accuracy?

Thank you.
Refer this answer on stack


  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..