[GitHub] spark pull request: [SPARK-14489][SPARK-14153][ML][PYSPARK] Suppor...

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

Github user MLnick commented on the pull request:

    https://github.com/apache/spark/pull/12577#issuecomment-216645240
  
    @sethah @holdenk @jkbradley I thought about this some more. I can't realistically think of a scenario apart from the ALS one where handling NaNs in the evaluator is desirable.
    
    So actually I think this should rather go into ALS itself - I'll call the param something like `unknownUserItemStrategy`. The default can be to return NaN as it does currently. We can make an option (perhaps called "skip" or "filter") that filters out NaNs in the prediction DF. This would allow this option to be used in cross-validation. This will also make it extensible for future potential additions such as using the "average user"  factor, or whatever other strategy.


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