Github user srowen commented on the issue:
https://github.com/apache/spark/pull/17556
The bucketing is trying to to bucket into buckets of equal P(x). It's a
condition on P(y | x). That said the right point isn't knowable from the
training data, and splitting to balance P(x) on either side of the split within
the bucket is perhaps the next-most principled thing to do.
To reach a conclusion though: if we have slightly more net preference for a
simple average, we could merge that change for now and decide later to make it
weighted.
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