Github user yanboliang commented on the pull request: https://github.com/apache/spark/pull/4911#issuecomment-77572142 @mengxr Yes, it make sense. After look through the code, I found we have two alternatives: 1, Implement a new PythonMLLibAPI looks like this def newGeneralizedLinearModel( modelClass: String, weights: Vector, intercept: Double): GeneralizedLinearModel { } And at pyspark we could get corresponding java model by this api and call save/load of java model which can make the model saved in Scala loadable in Python and vice versa. 2, Implement the save/load operation in Python independently which do the same thing as in Scala. I prefer the first one. Because in the second scenario, if we update the save/load in Scala we need to keep the save/load function in Python have the same behavior which may leads to inconsistent. Any comments and suggestions?
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