Github user manishamde commented on the pull request:
https://github.com/apache/spark/pull/3099#issuecomment-61916011
I have a few comments based upon the API:
1. Like @jkbradley, I prefer ```lr.setMaxIter(50)``` over
```lr.set(lr.maxIter, 50)```. Also, prefer to avoid passing parameters to fit
like ```lr.fit(dataset, lr.maxIter -> 50)```.
2. Constructors with getters and setters as @shivaram pointed will be
great. The LOC reduction is important and should not be discounted.
3. Do we plan to provided syntactic sugar such as a ```predict``` method
when we use ```model``` to transform a dataset? For me ```transform``` fits
well with the feature engineering stage and ```predict``` after the model
training has been performed.
4. It will be great to see the corresponding examples in Python.The
getter/setters would map well to Python properties. Also, it will be nice to
do an apples-to-apples comparison with the scikit-learn pipeline.
5. Finally, how do we plan to programatically answer (developer/user)
queries about algorithm properties such as multiclass classification support,
using internal storage format, etc.
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