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https://issues.apache.org/jira/browse/SPARK-7132?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14724889#comment-14724889
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Yanbo Liang commented on SPARK-7132:
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I will work on this issue.
[~josephkb]
I propose another way to resolve this issue.
The GBT Estimator remains take 1 input {DataFrame}, and we will split it into
training and validation dataset internal.
Because the runWithValidation interface will take RDD[LabeledPoint] as input,
it's easy to handle this.
And at the end of the GBT Estimator, we can also union these two dataset.
> Add fit with validation set to spark.ml GBT
> -------------------------------------------
>
> Key: SPARK-7132
> URL: https://issues.apache.org/jira/browse/SPARK-7132
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Reporter: Joseph K. Bradley
> Priority: Minor
>
> In spark.mllib GradientBoostedTrees, we have a method runWithValidation which
> takes a validation set. We should add that to the spark.ml API.
> This will require a bit of thinking about how the Pipelines API should handle
> a validation set (since Transformers and Estimators only take 1 input
> DataFrame). The current plan is to include an extra column in the input
> DataFrame which indicates whether the row is for training, validation, etc.
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