It is data-dependent, and hence needs hyper-parameter tuning, e.g.,
grid search. The first batch is certainly expensive. But after you
figure out a small range for each parameter that fits your data,
following batches should be not that expensive. There is an example
from AMPCamp:
http://ampcamp.berkeley.edu/5/exercises/movie-recommendation-with-mllib.html
-Xiangrui
On Tue, Nov 25, 2014 at 4:28 AM, Saurabh Agrawal
wrote:
>
>
> HI,
>
>
>
> I am trying to execute Collaborative filtering using MlLib. Can somebody
> please suggest how to calculate the following
>
>
>
> 1. Rank
>
> 2. Iterations
>
> 3. Lambda
>
>
>
> I understand these are adjustment factors and they help reduce the MSE in
> turn defining accuracy of algorithm but then is it all hit and trial or is
> there a definitive way to calculate them?
>
>
>
>
>
> Thanks!!
>
>
>
> Regards,
>
> Saurabh Agrawal
>
>
>
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