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https://issues.apache.org/jira/browse/SPARK-4766?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14323242#comment-14323242
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Joseph K. Bradley commented on SPARK-4766:
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[~prudenko] That's a great point about avoiding re-computing the same RDD when
doing cross-validation. I think it should be done by specializing
Pipeline.fit(Array[ParamMap]). I've made a JIRA for that (linked above).
> ML Estimator Params should subclass Transformer Params
> ------------------------------------------------------
>
> Key: SPARK-4766
> URL: https://issues.apache.org/jira/browse/SPARK-4766
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Affects Versions: 1.2.0
> Reporter: Joseph K. Bradley
>
> Currently, in spark.ml, both Transformers and Estimators extend the same
> Params classes. There should be one Params class for the Transformer and one
> for the Estimator, where the Estimator params class extends the Transformer
> one.
> E.g., it is weird to be able to do:
> {code}
> val model: LogisticRegressionModel = ...
> model.getMaxIter()
> {code}
> (This is the only case where this happens currently, but it is worth setting
> a precedent.)
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