The following code will allow you to run Logistic Regression using L-BFGS: val lbfgs = new LBFGS(new LogisticGradient(), new SquaredL2Updater()) lbfgs.setMaxNumIterations(numIterations).setRegParam(regParam).setConvergenceTol(tol).setNumCorrections(numCor)
val weights = lbfgs.optimize(data, initialWeights) The different loss function support you are asking for is the `new LogisticGradient()` part. The different regularizations support is the `new SquaredL2Updater()` The supported loss functions are: 1) Logistic - LogisticGradient 2) LeastSquares - LeastSquaresGradient 3) Hinge - HingeGradient The regularizers are: 0) No regularization - SimpleUpdater 1) L1 regularization - L1Updater 2) L2 regularization - SquaredL2Updater You can find more here: http://spark.apache.org/docs/latest/mllib-linear-methods.html#loss-functions I would suggest using L-BFGS rather than SGD as it's both much faster and more accurate. Burak ----- Original Message ----- From: "SK" <skrishna...@gmail.com> To: u...@spark.incubator.apache.org Sent: Thursday, August 7, 2014 6:31:14 PM Subject: [MLLib]:choosing the Loss function Hi, According to the MLLib guide, there seems to be support for different loss functions. But I could not find a command line parameter to choose the loss function but only found regType to choose the regularization. Does MLLib support a parameter to choose the loss function? thanks -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/MLLib-choosing-the-Loss-function-tp11738.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 --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
