Please see the current version of code for better documentation. https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
Sincerely, DB Tsai ---------------------------------------------------------- Blog: https://www.dbtsai.com PGP Key ID: 0xAF08DF8D On Tue, Jun 23, 2015 at 3:58 PM, DB Tsai <dbt...@dbtsai.com> wrote: > The regularization is handled after the objective function of data is > computed. See > https://github.com/apache/spark/blob/6a827d5d1ec520f129e42c3818fe7d0d870dcbef/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala > line 348 for L2. > > For L1, it's handled by OWLQN, so you don't see it explicitly, but the > code is in line 128. > > Sincerely, > > DB Tsai > ---------------------------------------------------------- > Blog: https://www.dbtsai.com > PGP Key ID: 0xAF08DF8D > > > On Tue, Jun 23, 2015 at 3:14 PM, Wei Zhou <zhweisop...@gmail.com> wrote: >> Hi DB Tsai, >> >> Thanks for your reply. I went through the source code of >> LinearRegression.scala. The algorithm minimizes square error L = 1/2n ||A >> weights - y||^2^. I cannot match this with the elasticNet loss function >> found here http://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html, which >> is the sum of square error plus L1 and L2 penalty. >> >> I am able to follow the rest of the mathematical deviation in the code >> comment. I am hoping if you could point me to any references that can fill >> this knowledge gap. >> >> Best, >> Wei >> >> >> >> 2015-06-19 12:35 GMT-07:00 DB Tsai <dbt...@dbtsai.com>: >>> >>> 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
