Github user sachingoel0101 commented on the pull request:
https://github.com/apache/flink/pull/772#issuecomment-108482112
Great. This is exactly what I had in mind.
There is perhaps another feature we could incorporate. Every algorithm has
some performance measure to so it can be evaluated on a test data set. We could
incorporate this as a parameter in the model. As soon as evaluate gets called,
this parameter is set to the performance value. It could be squared-error for
MLR, or F-score and accuracy, etc. for SVM, and so on.
User accesses this performance measure with a simple instance.get and (most
likely) prints it, so we don't need to make it of the same type across
different algorithms. Every Predictor can have its own performance object.
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