Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/14593
**[Test build #63583 has
finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/63583/consoleFull)**
for PR 14593 at commit
[`3cff947`](https://github.com/apache/spark/commit/3cff9477b10814d8fc9eeb27556b285e01d38956).
* This patch passes all tests.
* This patch merges cleanly.
* This patch adds the following public classes _(experimental)_:
*
`[Tokenization](http://en.wikipedia.org/wiki/Lexical_analysis#Tokenization) is
the process of taking text (such as a sentence) and breaking it into individual
terms (usually words). A simple
[Tokenizer](api/scala/index.html#org.apache.spark.ml.feature.Tokenizer) class
provides this functionality. The example below shows how to split sentences
into sequences of words.`
* ` * *(Breaking change)* The `apply` and `copy` methods for the case
class
[`BoostingStrategy`](api/scala/index.html#org.apache.spark.mllib.tree.configuration.BoostingStrategy)
have been changed because of a modification to the case class fields. This
could be an issue for users who use `BoostingStrategy` to set GBT parameters.`
* `* *(Breaking change)* The return value of
[`LDA.run`](api/scala/index.html#org.apache.spark.mllib.clustering.LDA) has
changed. It now returns an abstract class `LDAModel` instead of the concrete
class `DistributedLDAModel`. The object of type `LDAModel` can still be cast to
the appropriate concrete type, which depends on the optimization algorithm.`
* ` * In `DecisionTree`, the deprecated class method `train` has been
removed. (The object/static `train` methods remain.)`
* `* The `scoreCol` output column (with default value \"score\") was
renamed to be `probabilityCol` (with default value \"probability\"). The type
was originally `Double` (for the probability of class 1.0), but it is now
`Vector` (for the probability of each class, to support multiclass
classification in the future).`
* `labels - the number of times any class was predicted correctly (true
positives) normalized by the number of data`
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