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https://issues.apache.org/jira/browse/SPARK-7132?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Joseph K. Bradley resolved SPARK-7132.
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Resolution: Fixed
Fix Version/s: 2.4.0
Issue resolved by pull request 21129
[https://github.com/apache/spark/pull/21129]
> 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
> Assignee: Weichen Xu
> Priority: Minor
> Fix For: 2.4.0
>
>
> 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.
> Goals
> A [P0] Support efficient validation during training
> B [P1] Support early stopping based on validation metrics
> C [P0] Ensure validation data are preprocessed identically to training data
> D [P1] Support complex Pipelines with multiple models using validation data
> Proposal: column with indicator for train vs validation
> Include an extra column in the input DataFrame which indicates whether the
> row is for training or validation. Add a Param “validationFlagCol” used to
> specify the extra column name.
> A, B, C are easy.
> D is doable.
> Each estimator would need to have its validationFlagCol Param set to the same
> column.
> Complication: It would be ideal if we could prevent different estimators from
> using different validation sets. (Joseph: There is not an obvious way IMO.
> Maybe we can address this later by, e.g., having Pipelines take a
> validationFlagCol Param and pass that to the sub-models in the Pipeline.
> Let’s not worry about this for now.)
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