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There are some inconsistencies in the gradient boosting APIs. The target is a general boosting meta-algorithm, but the implementation is attached to trees. This was partially due to the delay of SPARK-1856. But for the 1.2 release, we should make the APIs consistent. 1. WeightedEnsembleModel -> private[tree] TreeEnsembleModel and renamed members accordingly. 1. GradientBoosting -> GradientBoostedTrees 1. Add RandomForestModel and GradientBoostedTreesModel and hide CombiningStrategy 1. Slightly refactored TreeEnsembleModel (Vote takes weights into consideration.) 1. Remove `trainClassifier` and `trainRegressor` from `GradientBoostedTrees` because they are the same as `train` 1. Rename class `train` method to `run` because it hides the static methods with the same name in Java. Deprecated `DecisionTree.train` class method. 1. Simplify BoostingStrategy and make sure the input strategy is not modified. Users should put algo and numClasses in treeStrategy. We create ensembleStrategy inside boosting. 1. Fix a bug in GradientBoostedTreesSuite with AbsoluteError 1. doc updates manishamde jkbradley Author: Xiangrui Meng <meng@databricks.com> Closes #3374 from mengxr/SPARK-4486 and squashes the following commits: 7097251 [Xiangrui Meng] address joseph's comments 98dea09 [Xiangrui Meng] address manish's comments 4aae3b7 [Xiangrui Meng] add RandomForestModel and GradientBoostedTreesModel, hide CombiningStrategy ea4c467 [Xiangrui Meng] fix unit tests 751da4e [Xiangrui Meng] rename class method train -> run 19030a5 [Xiangrui Meng] update boosting public APIs |
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