Github user VinceShieh commented on the issue:
https://github.com/apache/spark/pull/14640
Updates:
1. code refactoring. Rename the API to align with Sklearn changes
2. add implementation in CrossValidator
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Github user hqzizania commented on the issue:
https://github.com/apache/spark/pull/14640
This work may be similar with
[SPARK-8971](https://github.com/apache/spark/pull/14321) which is another
variation of KFold, and very significant in some cases. I suppose it is okay
to add to
Github user MechCoder commented on the issue:
https://github.com/apache/spark/pull/14640
Just FYI, we plan to rename "LabelKFold" to "GroupKFold" in the next
version of sklearn as a label can mean several things. (including the target
label)
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Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/14640
@VinceShieh there are kfold exists in Spark ML as well, and this PR could
maybe go instead of trying to add it on to mllib (for which we don't plan to
add new features anymore rather bug fixes
Github user VinceShieh commented on the issue:
https://github.com/apache/spark/pull/14640
@holdenk thanks for your comments. :) You are right. But as you can see,
this is a variant of kFold, so I think it's better to stay close to it,
otherwise, it would seems confusing, dont you
Github user VinceShieh commented on the issue:
https://github.com/apache/spark/pull/14640
if one understands the underlying ideas behind this method (labelKFold),
it's easy to take it as a class/category of data, though I do think it's not
that straightforward, even a bit confusing,
Github user amueller commented on the issue:
https://github.com/apache/spark/pull/14640
Do you guys thing "label" is a good name for this? Or did you just take it
from us? See the issue linked to above.
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Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/14640
Thanks for making this issue and PR :) The first thing before people are
likely to have the bandwith to review this is we are switching all new ML
development to Spark ML from MLlib so it might be
