FIX Bin training and validation data separately in GBDTs #13933
Merged
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Maybe we can have a compound entry that summaries all the small changes to |
|
I added a regular whatsnew entry, we can make the compound entry when there's more to add |
|
ping @adrinjalali @glemaitre |
…n_train_val_separately
|
Should the |
|
Yes, good point |
thomasjpfan
reviewed
May 27, 2019
sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py
Show resolved
Hide resolved
thomasjpfan
approved these changes
May 28, 2019
thomasjpfan
changed the title
koenvandevelde
pushed a commit
to koenvandevelde/scikit-learn
that referenced
this pull request
Jul 12, 2019
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Closes #13926
Instead of binning the whole data before the train/validation split, we now bin the training and validation data separately.
Not sure if that's worth a whatsnew entry, since it's all experimental still?