Github user yanboliang commented on the issue:
https://github.com/apache/spark/pull/16011
When I did QA work for 2.1, I found the parameter ``` handleInvalid``` is
disorienting. For example, the default behavior of ```QuantileDiscretizer``` to
handle invalid value (i.e. NaN) is throwing error. So users expect errors if
running against the following code.
```
val data = Array(-0.9, -0.5, -0.3, 0.0, 0.2, 0.5, 0.9, Double.NaN,
Double.NaN, Double.NaN)
val df = data.toSeq.toDF("input")
val discretizer = new QuantileDiscretizer()
.setInputCol("input")
.setOutputCol("result")
.setNumBuckets(3)
val model = discretizer.fit(df)
println(model.getSplits.mkString(","))
```
However, the above code can work well w/o any error thrown.
I'm OK with not make this change, but we should clarify it more clear in
document. Thanks.
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