Hi, I am running LogisticRegressionWithSGD in spark 1.4.1 and it always takes 100 iterations to train (which is the default). It never meets the convergence criteria, shouldn't the convergence criteria for SGD be based on difference in logloss or the difference in accuracy on a held out test set instead of the difference in weight vectors?
Code for convergence criteria: https://github.com/apache/spark/blob/c0e9ff1588b4d9313cc6ec6e00e5c7663eb67910/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala#L251 Thanks, Nishanth -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Stopping-criteria-for-gradient-descent-tp24727.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
