Github user facaiy commented on the issue:
https://github.com/apache/spark/pull/17503
Colsed since its duplicate PR #20632 has been merged.
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Github user AmplabJenkins commented on the issue:
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Github user facaiy commented on the issue:
https://github.com/apache/spark/pull/17503
HI, @WeichenXu123.
As said by @srowen , the benefit of this would be for speed at predict time
or for model storage. Hence I'm not sure whether benchmark is really need for
the PR.
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Github user WeichenXu123 commented on the issue:
https://github.com/apache/spark/pull/17503
Can you do some benchmark to show how much improvement this change will
bring ?
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Github user facaiy commented on the issue:
https://github.com/apache/spark/pull/17503
Hi, @yanboliang . Do you have time to take a look at first? Thanks very
much.
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Github user facaiy commented on the issue:
https://github.com/apache/spark/pull/17503
@jkbradley May you have time reviewing the pr? I believe that it will be a
little improvement for predict. Thanks.
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Github user HyukjinKwon commented on the issue:
https://github.com/apache/spark/pull/17503
(gentle ping @jkbradley)
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Github user sethah commented on the issue:
https://github.com/apache/spark/pull/17503
I think the benefit of this would be for speed at predict time or for model
storage. @srowen the nodes don't have to be equal to be merged, they just have
to output the same prediction. Since this a
Github user facaiy commented on the issue:
https://github.com/apache/spark/pull/17503
I have the same question with you. I guess that Impurity info is useful to
debug and analysis tree model. However, as tree is grown from root to leaf when
training, hence it seems needless to merge
Github user srowen commented on the issue:
https://github.com/apache/spark/pull/17503
I was saying that I thought the nodes had more info than just the
majority-class prediction. If they did, then they're much more rarely
combinable, because they vary in more than just their
Github user facaiy commented on the issue:
https://github.com/apache/spark/pull/17503
@srowen I am not sure whether I understand your question clearly.
RandomForest uses LearningNode to construct tree model when training, and
convert them to Leaf or InternalNode at last. Hence, all
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/17503
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Github user srowen commented on the issue:
https://github.com/apache/spark/pull/17503
It looks reasonable though I don't feel qualified to review it. I thought
the nodes had more than just the majority class - like the empirical
distribution at the node? That would make them not
Github user facaiy commented on the issue:
https://github.com/apache/spark/pull/17503
@srowen Hi, could you review the PR? The PR is simple, though many code for
unit test are added. Thanks.
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Github user facaiy commented on the issue:
https://github.com/apache/spark/pull/17503
@jkbradley @hhbyyh Could you review the PR? thanks.
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Github user AmplabJenkins commented on the issue:
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