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@keypointt keypointt commented Mar 8, 2017

What changes were proposed in this pull request?

RandomForest R Wrapper and GBT R Wrapper return param maxDepth to R models.

Below 4 R wrappers are changed:

  • RandomForestClassificationWrapper
  • RandomForestRegressionWrapper
  • GBTClassificationWrapper
  • GBTRegressionWrapper

How was this patch tested?

Test manually on my local machine.

@SparkQA
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SparkQA commented Mar 8, 2017

Test build #74197 has finished for PR 17207 at commit 82612dd.

  • This patch passes all tests.
  • This patch merges cleanly.
  • This patch adds no public classes.

@felixcheung
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Can you clarify? this is on the model summary, not a param?

@keypointt
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sorry being vague, I updated the PR description just now

yes, it's for R model summary, R wrapper returning maxDepth to R model
as mentioned in the comment at https://issues.apache.org/jira/browse/SPARK-19282, I don't know how to find the RandomForestRegressionModel in the R docs. R does take the parameter, but it is unclear if it exposes it on the model.

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SparkQA commented Mar 8, 2017

Test build #74218 has finished for PR 17207 at commit d16b65c.

  • This patch passes all tests.
  • This patch merges cleanly.
  • This patch adds no public classes.

@felixcheung
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ok, great, could you update PR title and JIRA title then.

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@keypointt keypointt changed the title [SPARK-19282][ML][SparkR] Expose model param "maxDepth" for R MLlib tree ensemble [SPARK-19282][ML][SparkR] RandomForest Wrapper and GBT Wrapper return param "maxDepth" to R models Mar 9, 2017
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SparkQA commented Mar 9, 2017

Test build #74250 has finished for PR 17207 at commit 477c4bc.

  • This patch passes all tests.
  • This patch merges cleanly.
  • This patch adds no public classes.

@keypointt
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Jenkins, retest this please

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SparkQA commented Mar 9, 2017

Test build #74271 has finished for PR 17207 at commit 477c4bc.

  • This patch passes all tests.
  • This patch merges cleanly.
  • This patch adds no public classes.

@keypointt
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ping @felixcheung

@felixcheung
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merged to master.

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3 participants