Hi Wei, I don't think ML is meant for single node computation, and the algorithms in ML are designed for pipeline framework.
In short, the lasso regression in ML is new algorithm implemented from scratch, and it's faster, and converged to the same solution as R's glmnet but with scalability. Here is the talk I gave in Spark summit about the new elastic-net feature in ML. I will encourage you to try the one ML. http://www.slideshare.net/dbtsai/2015-06-largescale-lasso-and-elasticnet-regularized-generalized-linear-models-at-spark-summit Sincerely, DB Tsai ---------------------------------------------------------- Blog: https://www.dbtsai.com PGP Key ID: 0xAF08DF8D On Fri, Jun 19, 2015 at 11:38 AM, Wei Zhou <zhweisop...@gmail.com> wrote: > Hi Spark experts, > > I see lasso regression/ elastic net implementation under both MLLib and ML, > does anyone know what is the difference between the two implementation? > > In spark summit, one of the keynote speakers mentioned that ML is meant for > single node computation, could anyone elaborate this? > > Thanks. > > Wei --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
