bo song created SPARK-17987:
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Summary: ML Evaluator fails to handle null values in the dataset
Key: SPARK-17987
URL: https://issues.apache.org/jira/browse/SPARK-17987
Project: Spark
Issue Type: Improvement
Components: ML
Affects Versions: 2.0.1, 1.6.2
Reporter: bo song
Take the RegressionEvaluator as an example, when the predictionCol is null in a
row, en exception "scala.MatchEror" will be thrown. The missing null prediction
is a common case, for example when an predictor is missing, or its value is out
of bound, almost machine learning models could not produce correct predictions,
then null predictions would be returned. Evaluators should handle the null
values instead of an exception thrown, the common way to handle missing null
values is to ignore them. Besides of the null value, the NAN value need to be
handled correctly too.
Those three evaluators RegressionEvaluator, BinaryClassificationEvaluator and
MulticlassClassificationEvaluator have the same problem.
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