The test accuracy doesn't mean the total loss. All points between (-1, 1) can separate points -1 and +1 and give you 1.0 accuracy, but their coressponding loss are different. -Xiangrui
On Sun, Sep 28, 2014 at 2:48 AM, Yanbo Liang <yanboha...@gmail.com> wrote: > Hi > > We have used LogisticRegression with two different optimization method SGD > and LBFGS in MLlib. > With the same dataset and the same training and test split, but get > different weights vector. > > For example, we use > spark-1.1.0/data/mllib/sample_binary_classification_data.txt as our training > and test dataset. > With LogisticRegressionWithSGD and LogisticRegressionWithLBFGS as training > method and the same other parameters. > > The precisions of these two methods almost near 100% and AUCs are also near > 1.0. > As far as I know, the convex optimization problem will converge to the > global minimum value. (We use SGD with mini batch fraction as 1.0) > But I got two different weights vector? Is this expectation or make sense? --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
