Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/16028
**[Test build #78743 has
finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/78743/testReport)**
for PR 16028 at commit
[`759b036`](https://github.com/apache/spark/commit/759b03661d9ab3e45a234fc468ba39e94584bf57).
* This patch **fails PySpark pip packaging tests**.
* This patch merges cleanly.
* This patch adds the following public classes _(experimental)_:
* ` ParamDesc[String](\"probabilityCol\", \"Column name for
predicted class conditional\" +`
* ` ParamDesc[Array[Double]](\"thresholds\", \"Thresholds in
multi-class classification\" +`
* ` final val probabilityCol: Param[String] = new Param[String](this,
\"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\")`
* ` final val thresholds: DoubleArrayParam = new DoubleArrayParam(this,
\"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 excepting that at most one value may be 0.
The class with largest value p/t is predicted, where p is the original
probability of that class and t is the class's threshold\", (t: Array[Double])
=> t.forall(_ >= 0) && t.count(_ == 0) <= 1)`
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