I ran into this problem too - it's because WeightedLeastSquares (added in 1.6.0 SPARK-10668) is being used on an ill-conditioned problem (SPARK-11918). I guess because of the one hot encoding. To get around it you need to ensure WeightedLeastSquares isn't used. Set parameters to make the following false:
$(solver) == "auto" && $(elasticNetParam) == 0.0 && numFeatures <= WeightedLeastSquares.MAX_NUM_FEATURES) || $(solver) == "normal" Hope this helps -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Trainning-a-spark-ml-linear-regresion-model-fail-after-migrating-from-1-5-2-to-1-6-1-tp27111p27128.html Sent from the Apache Spark User List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org