Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/12577#discussion_r60705650
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/evaluation/RegressionEvaluator.scala
---
@@ -69,7 +69,27 @@ final class RegressionEvaluator @Since("1.4.0")
(@Since("1.4.0") override val ui
@Since("1.4.0")
def setLabelCol(value: String): this.type = set(labelCol, value)
- setDefault(metricName -> "rmse")
+ /**
+ * Param for whether to drop rows where 'predictionCol' is NaN. NOTE -
only set this to
+ * true if you are certain that NaN predictions should be ignored!
+ * (default: false)
+ *
+ * @group expertParam
+ */
+ @Since("2.0.0")
+ val dropNaN: BooleanParam = new BooleanParam(this, "dropNaN",
--- End diff --
Currently if `nulls` are present `RegressionEvaluator` throws a
`MatchError`. I think we should either (a) disallow nullable columns explicitly
with a schema check in `evaluate` - this can then provide a more understandable
error message too; (b) allow nulls, but ignore them for both prediction and
label col.
I think nulls in the input for this case are unlikely and probably a result
of bad data or user error somewhere along the line. So I'd prefer option (a).
This then means the `dropNaN` setting will only apply to NaNs.
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