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 <saurabh.agra...@markit.com> 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 > > > ________________________________ > This e-mail, including accompanying communications and attachments, is > strictly confidential and only for the intended recipient. Any retention, > use or disclosure not expressly authorised by Markit is prohibited. This > email is subject to all waivers and other terms at the following link: > http://www.markit.com/en/about/legal/email-disclaimer.page > > Please visit http://www.markit.com/en/about/contact/contact-us.page? for > contact information on our offices worldwide. > > MarkitSERV Limited has its registered office located at Level 4, Ropemaker > Place, 25 Ropemaker Street, London, EC2Y 9LY and is authorized and regulated > by the Financial Conduct Authority with registration number 207294 --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
