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
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,
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?
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