Try many combinations of parameters on a small dataset, find the best, and then try to map them to a big dataset. You can also reduce the search region iteratively based on the best combination in the current iteration. -Xiangrui
On Wed, Aug 13, 2014 at 1:13 AM, Hoai-Thu Vuong <thuv...@gmail.com> wrote: > Thank you very much. I've read this tutorial, and understand what they've > done. However, the ranks set, or number of iterations set is human defined, > we can not sure the optimal value is in these set. By the way, I may expect > or do some wrong thing, should find the best model. > > > On Wed, Aug 13, 2014 at 1:26 PM, Xiangrui Meng <men...@gmail.com> wrote: >> >> You can define an evaluation metric first and then use a grid search >> to find the best set of training parameters. Ampcamp has a tutorial >> showing how to do this for ALS: >> >> http://ampcamp.berkeley.edu/big-data-mini-course/movie-recommendation-with-mllib.html >> -Xiangrui >> >> On Tue, Aug 12, 2014 at 8:01 PM, Hoai-Thu Vuong <thuv...@gmail.com> wrote: >> > In MLLib, I found the method to train matrix factorization model to >> > predict >> > the taste of user. In this function, there are some parameters such as >> > lambda, and rank, I can not find the best value to set these parameters >> > and >> > how to optimize this value. Could you please give me some recommends? >> > >> > -- >> > Thu. > > > > > -- > Thu. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
