Yes, WeightedLeastSquares can not solve some ill-conditioned problem currently, the community members have paid some efforts to resolve it (SPARK-13777). For the work around, you can set the solver to "l-bfgs" which will train the LogisticRegressionModel by L-BFGS optimization method.
2016-06-09 7:37 GMT-07:00 chaz2505 <chaz2...@hotmail.com>: > 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 > >
