Sean, Thanks a lot for your reply!
A few follow up questions: 1. numIterations should be 100, not 100*trainingSetSize, right? 2. My training set has 90k positive data points (with label 1) and 60k negative data points (with label 0). I set my numIterations to 100 as default. I still got the same predication result: it all predicted to label 1. And I'm sure my dataset is linearly separable because it has been run on other frameworks like scikit-learn. // code val numIterations = 100; val regParam = 1 val svm = new SVMWithSGD() svm.optimizer.setNumIterations(numIterations).setRegParam(regParam) svm.setIntercept(true) val model = svm.run(training) ----- Thanks! -Caron -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/SVMWithSGD-default-threshold-tp18645p18741.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
