[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, 6 insertions, 3 deletions

diff --git a/docs/mllib-classification-regression.md b/docs/mllib-classification-regression.md
index 5b9b4dd83b..8e91d62f4a 100644
--- a/docs/mllib-classification-regression.md
+++ b/docs/mllib-classification-regression.md
@@ -17,13 +17,13 @@ the supported algorithms for each type of problem.
   </thead>
   <tbody>
     <tr>
-      <td>Binary Classification</td><td>linear SVMs, logistic regression, decision trees, naive Bayes</td>
+      <td>Binary Classification</td><td>linear SVMs, logistic regression, decision trees, random forests, gradient-boosted trees, naive Bayes</td>
     </tr>
     <tr>
-      <td>Multiclass Classification</td><td>decision trees, naive Bayes</td>
+      <td>Multiclass Classification</td><td>decision trees, random forests, naive Bayes</td>
     </tr>
     <tr>
-      <td>Regression</td><td>linear least squares, Lasso, ridge regression, decision trees, isotonic regression</td>
+      <td>Regression</td><td>linear least squares, Lasso, ridge regression, decision trees, random forests, gradient-boosted trees, isotonic regression</td>
     </tr>
   </tbody>
 </table>
@@ -34,5 +34,8 @@ More details for these methods can be found here:
   * [binary classification (SVMs, logistic regression)](mllib-linear-methods.html#binary-classification)
   * [linear regression (least squares, Lasso, ridge)](mllib-linear-methods.html#linear-least-squares-lasso-and-ridge-regression)
 * [Decision trees](mllib-decision-tree.html)
+* [Ensembles of decision trees](mllib-ensembles.html)
+  * [random forests](mllib-ensembles.html#random-forests)
+  * [gradient-boosted trees](mllib-ensembles.html#gradient-boosted-trees-gbts)
 * [Naive Bayes](mllib-naive-bayes.html)
 * [Isotonic regression](mllib-isotonic-regression.html)