Github user tewf commented on the issue:
https://github.com/apache/spark/pull/14326
Could we instead implement a more general Robust Linear Model
[M-estimator](http://research.microsoft.com/en-us/um/people/zhang/INRIA/Publis/Tutorial-Estim/node24.html)
type like is done in [statsmodels
RLM](http://statsmodels.sourceforge.net/0.6.0/rlm.html), see
[RLM.py](http://statsmodels.sourceforge.net/0.6.0/_modules/statsmodels/robust/robust_linear_model.html#RLM)?
The Huber loss would then be one of the M-estimators, maybe the default as
done in statsmodels.
I think that the
[IterativelyReweightedLeastSquares](https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/optim/IterativelyReweightedLeastSquares.scala)
was made and intended to aid in developing a robust M-Estimator framework.
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