Can you check the loss of both LBFGS and SGD implementation? One reason maybe SGD doesn't converge well and you can see that by comparing both log-likelihoods. One other potential reason maybe the label of your training data is totally separable, so you can always increase the log-likelihood by multiply a constant to the weights.
Sincerely, DB Tsai ------------------------------------------------------- My Blog: https://www.dbtsai.com LinkedIn: https://www.linkedin.com/in/dbtsai On Sun, Sep 28, 2014 at 11:48 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
