[SPARK-5974] [SPARK-5980] [mllib] [python] [docs] Update ML guide with save/load, Python GBT

* Add GradientBoostedTrees Python examples to ML guide
  * I ran these in the pyspark shell, and they worked.
* Add save/load to examples in ML guide
* Added note to python docs about predict,transform not working within RDD actions,transformations in some cases (See SPARK-5981)

CC: mengxr

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #4750 from jkbradley/SPARK-5974 and squashes the following commits:

c410e38 [Joseph K. Bradley] Added note to LabeledPoint about attributes
bcae18b [Joseph K. Bradley] Added import of models for save/load examples in ml guide.  Fixed line length for tree.py, feature.py (but not other ML Pyspark files yet).
6d81c3e [Joseph K. Bradley] completed python GBT examples
9903309 [Joseph K. Bradley] Added note to python docs about predict,transform not working within RDD actions,transformations in some cases
c7dfad8 [Joseph K. Bradley] Added model save/load to ML guide.  Added GBT examples to ML guide

1 files changed, 9 insertions, 0 deletions

diff --git a/docs/mllib-collaborative-filtering.md b/docs/mllib-collaborative-filtering.md
index ef18cec937..935cd8dad3 100644
--- a/docs/mllib-collaborative-filtering.md
+++ b/docs/mllib-collaborative-filtering.md
@@ -66,6 +66,7 @@ recommendation model by measuring the Mean Squared Error of rating prediction.
 
 {% highlight scala %}
 import org.apache.spark.mllib.recommendation.ALS
+import org.apache.spark.mllib.recommendation.MatrixFactorizationModel
 import org.apache.spark.mllib.recommendation.Rating
 
 // Load and parse the data
@@ -95,6 +96,9 @@ val MSE = ratesAndPreds.map { case ((user, product), (r1, r2)) =>
   err * err
 }.mean()
 println("Mean Squared Error = " + MSE)
+
+model.save("myModelPath")
+val sameModel = MatrixFactorizationModel.load("myModelPath")
 {% endhighlight %}
 
 If the rating matrix is derived from another source of information (e.g., it is inferred from
@@ -181,6 +185,9 @@ public class CollaborativeFiltering {
       }
     ).rdd()).mean();
     System.out.println("Mean Squared Error = " + MSE);
+
+    model.save("myModelPath");
+    MatrixFactorizationModel sameModel = MatrixFactorizationModel.load("myModelPath");
   }
 }
 {% endhighlight %}
@@ -191,6 +198,8 @@ In the following example we load rating data. Each row consists of a user, a pro
 We use the default ALS.train() method which assumes ratings are explicit. We evaluate the
 recommendation by measuring the Mean Squared Error of rating prediction.
 
+Note that the Python API does not yet support model save/load but will in the future.
+
 {% highlight python %}
 from pyspark.mllib.recommendation import ALS, Rating