Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/7099#discussion_r33719046
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
---
@@ -31,7 +31,8 @@ import org.apache.spark.sql.DataFrame
* @param predictionAndObservations an RDD of (prediction, observation)
pairs.
*/
@Experimental
-class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)])
extends Logging {
+class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)])
+ extends Logging with Serializable {
--- End diff --
I think the serialization errors are from LinearRegressionModel being used
in closures (for prediction). That should be fixable by having trainingResults
be transient. We'll have to add doc to the getter/setter for it to warn the
user about using those only on the driver.
I'd strongly prefer lazy evaluation.
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