ML Estimator Params should be distinct from Transformer Params

Currently, in spark.ml, both Transformers and Estimators extend the same Params classes. There should be one Params class for the Transformer and one for the Estimator. These could sometimes be the same, but for other models, we may need either (a) to make them distinct or (b) to have the Estimator params class extend the Transformer one.

E.g., it is weird to be able to do:

val model: LogisticRegressionModel = ...
model.getMaxIter()

It's also weird to be able to:

• Wrap LogisticRegressionModel (a Transformer) with CrossValidator
• Pass a set of ParamMaps to CrossValidator which includes parameter LogisticRegressionModel.maxIter
• (CrossValidator would try to set that parameter.)
• I'm not sure if this would cause a failure or just be a noop.