-
Notifications
You must be signed in to change notification settings - Fork 64
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Using sWeights with GBReweighter #60
Labels
Comments
Hi, From your description it sounds like sweights change distribution so much that it's effective support becomes smaller than target (regions with too small or negative 'weight'). If it's not the case, then try simpler model (i.e. default parameters). |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Hi,
I've noticed some issues with very large weights using GBReweighter. I am trying to reweight data to look like some toy data I have. I have signal sWeights for the data but not for the toy data.
I've trained the BDT using only the original_weight but not target_weight arguments. Reading previous issues I've tried to ensure that there is overlap in my data and toy distributions. I'm using 800k toy and 500k data events which I think should be enough.
Do I need sWeights information for both datasets?
Without the sWeights the reweighting is much better, however how should I account for background if I don't use sWeights?
Thanks
The text was updated successfully, but these errors were encountered: