[SPARK-4197] [mllib] GradientBoosting API cleanup and examples in Scala, Java

### Summary

* Made it easier to construct default Strategy and BoostingStrategy and to set parameters using simple types.
* Added Scala and Java examples for GradientBoostedTrees
* small cleanups and fixes

### Details

GradientBoosting bug fixes (“bug” = bad default options)
* Force boostingStrategy.weakLearnerParams.algo = Regression
* Force boostingStrategy.weakLearnerParams.impurity = impurity.Variance
* Only persist data if not yet persisted (since it causes an error if persisted twice)

BoostingStrategy
* numEstimators: renamed to numIterations
* removed subsamplingRate (duplicated by Strategy)
* removed categoricalFeaturesInfo since it belongs with the weak learner params (since boosting can be oblivious to feature type)
* Changed algo to var (not val) and added @BeanProperty, with overload taking String argument
* Added assertValid() method
* Updated defaultParams() method and eliminated defaultWeakLearnerParams() since that belongs in Strategy

Strategy (for DecisionTree)
* Changed algo to var (not val) and added @BeanProperty, with overload taking String argument
* Added setCategoricalFeaturesInfo method taking Java Map.
* Cleaned up assertValid
* Changed val’s to def’s since parameters can now be changed.

CC: @manishamde @mengxr @codedeft
Note on binary compatibility: If we add new parameters later, it will change the constructor signature. Then we need to provide auxiliary constructors to maintain binary compatibility, which makes the code hard to maintain in the long run. We can hide constructors with parameters and only expose the one without any parameters. Then check the required parameters at runtime by adding `def validate()`.