[SPARK-5436] [MLlib] Validate GradientBoostedTrees using runWithValidation

One can early stop if the decrease in error rate is lesser than a certain tol or if the error increases if the training data is overfit.

This introduces a new method runWithValidation which takes in a pair of RDD's , one for the training data and the other for the validation.

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #4677 from MechCoder/spark-5436 and squashes the following commits:

1bb21d4 [MechCoder] Combine regression and classification tests into a single one
e4d799b [MechCoder] Addresses indentation and doc comments
b48a70f [MechCoder] COSMIT
b928a19 [MechCoder] Move validation while training section under usage tips
fad9b6e [MechCoder] Made the following changes 1. Add section to documentation 2. Return corresponding to bestValidationError 3. Allow negative tolerance.
55e5c3b [MechCoder] One liner for prevValidateError
3e74372 [MechCoder] TST: Add test for classification
77549a9 [MechCoder] [SPARK-5436] Validate GradientBoostedTrees using runWithValidation

1 files changed, 11 insertions, 0 deletions

diff --git a/docs/mllib-ensembles.md b/docs/mllib-ensembles.md
index fb90b70399..00040e6073 100644
--- a/docs/mllib-ensembles.md
+++ b/docs/mllib-ensembles.md
@@ -427,6 +427,17 @@ We omit some decision tree parameters since those are covered in the [decision t
 
 * **`algo`**: The algorithm or task (classification vs. regression) is set using the tree [Strategy] parameter.
 
+#### Validation while training
+
+Gradient boosting can overfit when trained with more trees. In order to prevent overfitting, it is useful to validate while
+training. The method runWithValidation has been provided to make use of this option. It takes a pair of RDD's as arguments, the
+first one being the training dataset and the second being the validation dataset.
+
+The training is stopped when the improvement in the validation error is not more than a certain tolerance
+(supplied by the `validationTol` argument in `BoostingStrategy`). In practice, the validation error
+decreases initially and later increases. There might be cases in which the validation error does not change monotonically,
+and the user is advised to set a large enough negative tolerance and examine the validation curve to to tune the number of
+iterations.
 
 ### Examples