Thanks Upul. So, are you thinking along the lines of performance? Sure, I'll run a test.
On Sun, May 31, 2015 at 9:50 PM, Upul Bandara <[email protected]> wrote: > If it is possible, I would like to have both. > > L-BFGS converges faster than SGD. But it goes through the entire data set > before moving from one iteration to the next. > Whereas, SGD uses a minit-batch of the training data set for calculating > and updating its gradient. > Hence, for large data sets SGD is more practical than L-BFGS. > > I think we can test this scenario by running these two algorithms against > a large data set (~ 1GB) > > Thanks, > Upul > > On Sun, May 31, 2015 at 8:02 PM, Nirmal Fernando <[email protected]> wrote: > >> One other benefit of switching is, this API supports multi-class >> classification too. I've tested this API with Iris dataset. >> >> On Sun, May 31, 2015 at 7:33 PM, Nirmal Fernando <[email protected]> wrote: >> >>> Hi, >>> >>> Currently in ML, we use mini-batch gradient descent algorithm when >>> running logistic regression. But Spark-mllib recommends L-BFGS over >>> mini-batch gradient descent for faster convergence [1]. >>> >>> I tested both the implementation with the same dataset and gained an >>> improved accuracy in L-BFGS (80% vs 67% for SGD). >>> >>> Shall we switch? >>> >>> [1] >>> https://spark.apache.org/docs/latest/mllib-linear-methods.html#logistic-regression >>> >>> >>> -- >>> >>> Thanks & regards, >>> Nirmal >>> >>> Associate Technical Lead - Data Technologies Team, WSO2 Inc. >>> Mobile: +94715779733 >>> Blog: http://nirmalfdo.blogspot.com/ >>> >>> >>> >> >> >> -- >> >> Thanks & regards, >> Nirmal >> >> Associate Technical Lead - Data Technologies Team, WSO2 Inc. >> Mobile: +94715779733 >> Blog: http://nirmalfdo.blogspot.com/ >> >> >> > > > -- > Upul Bandara, > Associate Technical Lead, WSO2, Inc., > Mob: +94 715 468 345. > -- Thanks & regards, Nirmal Associate Technical Lead - Data Technologies Team, WSO2 Inc. Mobile: +94715779733 Blog: http://nirmalfdo.blogspot.com/
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