[GitHub] spark issue #18313: [SPARK-21087] [ML] CrossValidator, TrainValidationSplit ...

[jkbradley <gi...@git.apache.org>]

Github user jkbradley commented on the issue:

    https://github.com/apache/spark/pull/18313
  
    Oh, you're right; I overlooked that it only holds all of the models for a single split.  In that case, I agree it could be problematic to keep all in memory by default.  How does this sound then: We can do 2 separate PRs, each of which adds a separate option:
    1. Add an option for keeping the models in memory so that they are available as a data field in the Model object after training.  This caters to smaller use cases, focusing on ease-of-use.
    2. (your PR) Add an option to dump models to a directory, not keeping them in memory.  This caters to big use cases, focusing on scalability.
    
    What do you think?  If this sounds good, feel free to reopen your current PR.


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