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CC @dbtsai in case you're interested b/c of Sequoia forests
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After discussion and modifications, I approve this PR overall. Ping
@jkbradley Can you take a look now ?
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Made a few updates, hereâs a quick summary/what Iâd propose moving
forward:
Right now:
* Shared row indices for all (categorical & continuous) features are stored
& updated in `Tr
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> We'll actually only have to run an O(n log n) sort on continuous feature
values once (i.e. in the FeatureVector constructor), since once the continuous
features are sorted we can update them
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@WeichenXu123 Thanks for the comments! I'll respond inline:
> In your doc, you said "Specifically, we only need to store sufficient
stats for each bin of a single feature, as opposed to ea
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@smurching I found some issues and have some thoughts on the columnar
features format:
- In your doc, you said "Specifically, we only need to store sufficient
stats for each bin of a s
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Sorry, realized I conflated feature subsampling and `subsampleWeights`
(instance weights for training examples). IMO feature subsampling can be added
in a follow-up PR, but `subsampleWeights` shou
Github user smurching commented on the issue:
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Thanks for the comments!
- Yep, feature subsampling is necessary for using local tree training in
distributed training. I was thinking of adding subsampling in a follow-up PR.
You're righ
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I made a rough pass. I have only a few issues for now, I haven't go into
code details:
- The `colStoreInit` currently ignore the `subsampleWeights`, it should be
used, isn't it ? I rea
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The failing SparkR test (which compares `RandomForest` predictions to
hardcoded values) fails not due to a correctness issue but (AFAICT) because of
an implementation change in best-split selectio
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The failing tests (in `DecisionTreeSuite`) fail because we've historically
handled
a) splits that have 0 gain
differently from
b) splits that fail to achieve user-specif
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add to whitelist
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