Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/6296#discussion_r30760842
--- Diff: docs/ml-ensembles.md ---
@@ -0,0 +1,73 @@
+---
+layout: global
+title: Ensembles
+displayTitle: <a href="ml-guide.html">ML</a> - Ensembles
+---
+
+* Table of contents
+{:toc}
+
+An [ensemble method](http://en.wikipedia.org/wiki/Ensemble_learning)
+is a learning algorithm which creates a model composed of a set of other
base models.
+ML supports the following ensemble algorithms:
[`OneVsRest`](api/scala/index.html#org.apache.spark.ml.classifier.OneVsRest)
+
+## OneVsRest
+
+[OneVsRest](http://en.wikipedia.org/wiki/Multiclass_classification#One-vs.-rest)
is an example of a machine learning reduction for performing multiclass
classification given a base classifier that can perform binary classification
efficiently.
+
+`OneVsRest` is implemented as an `Estimator` takes as base classifier
instances of `Classifier` and creates a binary classification problem for each
of the k classes. The classifier for class i is trained to predict whether the
label is i or not, distinguishing class i from all other classes.
+
+Predictions are done by evaluating each binary classifier and the index of
the most confident classifier is output as label.
+
+### Example
+
+The example below demonstrates how to load a
+[LIBSVM data
file](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/), parse it as an
RDD of `LabeledPoint` and perform multiclass classification using `OneVsRest`.
The test error is calculated to measure the algorithm accuracy.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+import org.apache.spark.ml.classification.{LogisticRegression, OneVsRest}
+import org.apache.spark.ml.util.MetadataUtils
+import org.apache.spark.mllib.evaluation.MulticlassMetrics
+import org.apache.spark.mllib.util.MLUtils
+import org.apache.spark.sql.{Row, SQLContext}
+import org.apache.spark.sql.functions._
+
+val sqlContext = new SQLContext(sc)
+import sqlContext.implicits._
+
+// parse data into dataframe
+val data = MLUtils.loadLibSVMFile(sc,
"data/mllib/sample_multiclass_classification_data.txt")
+.toDF()
+.withColumn("rnd", rand(0))
--- End diff --
Use DataFrame.randomSplit instead
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