[MLLIB] SPARK-1547: Adding Gradient Boosting to MLlib

Given the popular demand for gradient boosting and AdaBoost in MLlib, I am creating a WIP branch for early feedback on gradient boosting with AdaBoost to follow soon after this PR is accepted. This is based on work done along with @hirakendu that was pending due to decision tree optimizations and random forests work.

Ideally, boosting algorithms should work with any base learners.  This will soon be possible once the MLlib API is finalized -- we want to ensure we use a consistent interface for the underlying base learners. In the meantime, this PR uses decision trees as base learners for the gradient boosting algorithm. The current PR allows "pluggable" loss functions and provides least squares error and least absolute error by default.

Here is the task list:
- [x] Gradient boosting support
- [x] Pluggable loss functions
- [x] Stochastic gradient boosting support – Re-use the BaggedPoint approach used for RandomForest.
- [x] Binary classification support
- [x] Support configurable checkpointing – This approach will avoid long lineage chains.
- [x] Create classification and regression APIs
- [x] Weighted Ensemble Model -- created a WeightedEnsembleModel class that can be used by ensemble algorithms such as random forests and boosting.
- [x] Unit Tests

Future work:
+ Multi-class classification is currently not supported by this PR since it requires discussion on the best way to support "deviance" as a loss function.
+ BaggedRDD caching -- Avoid repeating feature to bin mapping for each tree estimator after standard API work is completed.

cc: @jkbradley @hirakendu @mengxr @etrain @atalwalkar @chouqin
Yea, I guess from the design perspective, it's tempting to unify these under the same umbrella.

IMO, RandomForest is *mostly* a specific instance of a generic ensemble model, so this makes sense.

However, I think that boosted models have some specific things about them due to their sequential nature (as opposed to parallel nature of RandomForest). E.g., if you have 1000 models, you can potentially predict based on the *first* 100 models whereas with RandomForest you can pick any 100. You also have to do overfitting/underfitting analyses on boosted models sequentially, etc.