bo song created SPARK-17987: ------------------------------- 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org