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. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
