Hi! We've been using Spark 3.0.1 to train Logistic regression models with MLLIb. We've recently upgraded to Spark 3.3.0 without making any other code changes and noticed that the trained models are different as compared to the ones trained with 3.0.1 and therefore behave differently when used for prediction.
We went through the release notes and through the API changes to see if the default behaviour changed, but we could not find anything. Do you know what changed between version 3.01 and 3.3.0? And if so, how could we guarantee the same behaviour as in 3.0.1? Thanks for your help! Roger
