Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/9581#issuecomment-155260886
**[Test build #45459 has
finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/45459/consoleFull)**
for PR 9581 at commit
[`9138fba`](https://github.com/apache/spark/commit/9138fba068f2eac34419f4e9d95fdf47fc6d72ab).
* This patch **fails PySpark unit tests**.
* This patch merges cleanly.
* This patch adds the following public classes _(experimental)_:\n * `
* Sets the random seed (default: hash value of the class name).`\n * `class
BisectingKMeansModel @Since(\"1.6.0\") (`\n * ` probabilityCol =
Param(Params._dummy(), \"probabilityCol\", \"Column name for predicted class
conditional probabilities. Note: Not all models output well-calibrated
probability estimates! These probabilities should be treated as confidences,
not precise probabilities.\", str)`\n * ` self.probabilityCol =
Param(self, \"probabilityCol\", \"Column name for predicted class conditional
probabilities. Note: Not all models output well-calibrated probability
estimates! These probabilities should be treated as confidences, not precise
probabilities.\", str)`\n * ` 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.\", None)`\n * `
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.\", None)`\n
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