I think in the SGD algorithm, the mini batch sample is done without replacement. So with fraction=1, then all the rows will be sampled exactly once to form the miniBatch, resulting to the deterministic/classical case.
On Fri, Aug 7, 2015 at 9:05 AM, Feynman Liang <fli...@databricks.com> wrote: > Sounds reasonable to me, feel free to create a JIRA (and PR if you're up for > it) so we can see what others think! > > On Fri, Aug 7, 2015 at 1:45 AM, Gerald Loeffler > <gerald.loeff...@googlemail.com> wrote: >> >> hi, >> >> if new LinearRegressionWithSGD() uses a miniBatchFraction of 1.0, >> doesn’t that make it a deterministic/classical gradient descent rather >> than a SGD? >> >> Specifically, miniBatchFraction=1.0 means the entire data set, i.e. >> all rows. In the spirit of SGD, shouldn’t the default be the fraction >> that results in exactly one row of the data set? >> >> thank you >> gerald >> >> -- >> Gerald Loeffler >> mailto:gerald.loeff...@googlemail.com >> http://www.gerald-loeffler.net >> >> --------------------------------------------------------------------- >> 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
