Github user MLnick commented on the issue:
https://github.com/apache/spark/pull/12896
Another option is to make `predictionCol` nullable and return `null`
predictions. The `drop` strategy can still apply (though it will need to be a
custom `filter` rather than `df.na.drop`), but it makes it totally clear when a
prediction is "missing" vs `NaN`.
However, is it even possible to get a bunch of `NaN`s, e.g. if the model
somehow diverged (I don't think that's even possible with ALS?). So, this may
just add needless complexity.
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