Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/7099#discussion_r34088829
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala ---
@@ -212,12 +230,140 @@ class LinearRegressionModel private[ml] (
extends RegressionModel[Vector, LinearRegressionModel]
with LinearRegressionParams {
+ private var trainingSummary: Option[LinearRegressionTrainingSummary] =
None
--- End diff --
I see; I thought the use case was for sending stats to workers. For model
save/load, I agree we will need to save local stats by default. I'm not sure
that marking items as transient is the way to do that, unless we want to
encourage users to save models as POJOs (which we should not). I think that a
follow-up PR should add the summary to the saved model, where it saves only the
local values. When the model is loaded from disk, the values not saved can be
set to null. Is there a need to mark things as transient?
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