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Hi, I was wondering if there's any way of accessing the probabilities of each of the bagged models which are averaged to get the output of predict_proba() for the L1 models? This would be helpful to be able to calculate uncertainties for each of the models as well as uncertainty for the entire weighted ensemble
Thanks!
The text was updated successfully, but these errors were encountered:
Hi @amanmalali, this is a good feature request, and I've been playing around with ideas on how to add this in a nice way (want to also make the fold models viewable in .leaderboard() for example). This is on our radar to add in an upcoming release.
@amanmalali Thanks for your interest! if you have a branch with the changes, I could take a look as a reference. For this one it is a bit in-depth for the official implementation, since I want the fold models to also be viewable in predictor.leaderboard(..., show_folds=True).
For the implementation I would be basing things off of BaggedEnsembleModel.predict_proba_children to fetch the fold predictions. If you have an implementation for calculating uncertainty of a bagged model based on the child predictions, that could be valuable to share as a reference code implementation. We could add it as a new method to BaggedEnsembleModel, such as BaggedEnsembleModel.compute_uncertainty(X_holdout)
Hi, I was wondering if there's any way of accessing the probabilities of each of the bagged models which are averaged to get the output of
predict_proba()
for the L1 models? This would be helpful to be able to calculate uncertainties for each of the models as well as uncertainty for the entire weighted ensembleThanks!
The text was updated successfully, but these errors were encountered: