Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/10207#issuecomment-163087384
**[Test build #47384 has
finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/47384/consoleFull)**
for PR 10207 at commit
[`dc584b2`](https://github.com/apache/spark/commit/dc584b26e7c6c9e0bdab4e304377934adc015505).
* This patch passes all tests.
* This patch merges cleanly.
* This patch adds the following public classes _(experimental)_:\n *
`[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. It is also known as \"One-vs-All.\"`\n * `[Iris
dataset](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/iris.scale),
parse it as a DataFrame and perform multiclass classification using
`OneVsRest`. The test error is calculated to measure the algorithm accuracy.`\n
* `The Pipelines API for Decision Trees offers a bit more functionality than
the original API. In particular, for classification, users can get the
predicted probability of each class (a.k.a. class conditional
probabilities).`\n * `* a bit more functionality for random forests: estimates
of feature importance, as well as the predicted probability of each class
(a.k.a. class conditional
probabilities) for classification.`\n * `public class Document implements
Serializable `\n * `public class LabeledDocument extends Document implements
Serializable `\n * `public class Document implements Serializable `\n *
`public class LabeledDocument extends Document implements Serializable `\n
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