Hi, Trying to use LBFGS as the optimizer, do I need to implement feature scaling via StandardScaler or does LBFGS do it by default?
Following code generated error " Failure again! Giving up and returning, Maybe the objective is just poorly behaved ?". val data = sc.textFile("file:///data/Train/final2.train") val parsedata = data.map { line => val partsdata = line.split(',') LabeledPoint(partsdata(0).toDouble, Vectors.dense(partsdata(1).split(' ').map(_.toDouble))) } val train = parsedata.map(x => (x.label, MLUtils.appendBias(x.features))).cache() val numCorrections = 10 val convergenceTol = 1e-4 val maxNumIterations = 50 val regParam = 0.1 val initialWeightsWithIntercept = Vectors.dense(new Array[Double](2)) val (weightsWithIntercept, loss) = LBFGS.runLBFGS(train, new LeastSquaresGradient(), new SquaredL2Updater(), numCorrections, convergenceTol, maxNumIterations, regParam, initialWeightsWithIntercept) Did I implement LBFGS for Linear Regression via "LeastSquareGradient()" correctly? Thanks Tri