Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/4716#issuecomment-139648558
[Test build #42352 has
finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/42352/console)
for PR 4716 at commit
[`b81bb53`](https://github.com/apache/spark/commit/b81bb53ae79c19e2990a7781436b83d0b53ab1a4).
* This patch **fails Scala style tests**.
* This patch merges cleanly.
* This patch adds the following public classes _(experimental)_:
* `class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol,
HasLabelCol, HasPredictionCol,`
* `class MultilayerPerceptronClassificationModel(JavaModel):`
* `class MinMaxScaler(JavaEstimator, HasInputCol, HasOutputCol):`
* `class MinMaxScalerModel(JavaModel):`
* `class StringIndexer(JavaEstimator, HasInputCol, HasOutputCol,
HasHandleInvalid):`
* ` ("thresholds", "Thresholds in multi-class classification to
adjust the probability of " +`
* `class HasHandleInvalid(Params):`
* `class HasElasticNetParam(Params):`
* `class HasFitIntercept(Params):`
* `class HasStandardization(Params):`
* `class HasThresholds(Params):`
* ` thresholds = Param(Params._dummy(), "thresholds", "Thresholds in
multi-class classification to adjust the probability of predicting each class.
Array must have length equal to the number of classes, with values >= 0. The
class with largest value p/t is predicted, where p is the original probability
of that class and t is the class' threshold.")`
* ` self.thresholds = Param(self, "thresholds", "Thresholds in
multi-class classification to adjust the probability of predicting each class.
Array must have length equal to the number of classes, with values >= 0. The
class with largest value p/t is predicted, where p is the original probability
of that class and t is the class' threshold.")`
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