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From "Joseph K. Bradley (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-7132) Add fit with validation set to spark.ml GBT
Date Wed, 09 May 2018 17:21:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-7132?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Joseph K. Bradley reassigned SPARK-7132:
----------------------------------------

    Assignee: Weichen Xu

> 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
>
> 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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