Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/11447#issuecomment-199957859
@yanboliang I did some factoring of `naiveBayes` implementation in
https://github.com/apache/spark/pull/11890. I feel it makes the code less
coupled if we have separate wrappers for each algorithm we port to SparkR
instead of put everything under `SparkRWrappers`. If we store the training
DataFrame in the survival analysis wrapper, I think we don't need to create
AFTSurvivalRegressionSummary class.
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