# 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.

• So correct me If i'm wrong but if bootstrap=True then for every tree we pick random sample of data but rows from one tree may also present in another tree but if we choose bootstrap=False everything will be unique. If I'm not correct can you explain it in simpler term. – Sai Kumar Nov 18 '18 at 18:46