Created
February 21, 2017 00:34
-
-
Save mrocklin/19c89d78e34437e061876a9872f4d2df to your computer and use it in GitHub Desktop.
Is there a similar example for hyperparameter tuning for dask-Xgboost models?
Yes, I'd love to see that, too. Struggling to figure out how to do a GridSearch CV on a dask-XGBoost, either on the Dask-ML or the original XGBoost implementation.
Hi folks, I'm unlikely to be responsive here. I recommend creating a
github issue somewhere so that folks other than me can help out.
…On Mon, May 17, 2021 at 9:32 AM Richard Pelgrim ***@***.***> wrote:
***@***.**** commented on this gist.
------------------------------
Is there a similar example for hyperparameter tuning for dask-Xgboost
models?
Yes, I'd love to see that, too. Struggling to figure out how to do a
GridSearch CV on a dask-XGBoost, either on the Dask-ML or the original
XGBoost implementation.
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub
<https://gist.github.com/19c89d78e34437e061876a9872f4d2df#gistcomment-3745709>,
or unsubscribe
<https://github.com/notifications/unsubscribe-auth/AACKZTB5P7EHDZB5AG6RS33TOESJDANCNFSM4OWMEGRA>
.
Great example!
Consider using an ordinal encoding or native categorical support in xgboost instead of a one hot encoding. Generally, a one hot encoding is slower on large datasets and often results in a less skillful xgboost model.
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Is there a similar example for hyperparameter tuning for dask-Xgboost models?