Github user jkbradley commented on the pull request: https://github.com/apache/spark/pull/3637#issuecomment-70022005 @etrain Thanks for the feedback! It would be great to hear thoughts about what could be made easier. Some questions are: * Is it easy to implement a new algorithm? * IMO, LinearRegression shows it can require very little code. But what non-obvious items make it hard? Some possibilities in my mind are: * adding setter methods for parameters * passing type parameters to abstract classes * implementing copy() * optimizing transform() to be faster than the default implementation * Is all of the functionality needed? (E.g., do we not want the various output columns?) * I have a hard time thinking of an algorithm which does not have these concepts, or where it would be difficult to implement them. @tomerk Thanks for the feedback! Some responses: * Typed vs. Untyped Estimators: The conversation was moved to the JIRA (though mainly in the design doc linked from there). Basically, it was decided not to have strongly typed public interfaces. However, I kept the developer API (protected) strongly typed interfaces for prediction, where I agree it is useful. For training/fitting, I removed the typed interface since it is less useful and also requires passing around more type parameters (FeaturesType, etc.). * Wrapper for fit/transform so developers do not have to call transformSchema and combine ParamMaps: IMO, the current default transform() implementation suffices. For fit(), I agree it may be good to include; Iâll add that. * Parameterize sharedParams mixing to eliminate need for setters: I think itâs a good suggestion. It would make declaring mixing awkward but would be shorter overall. @etrain What do you think? * ScalaReflection: The main issues are with the Java API. Ideally, developers will be able to write new Estimators/Models in Java. But SQL reflection works differently there (for now), which is why we specify the DataType in the first place. Java also has trouble understanding this.type. I'll push an update soon.
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