It's something like the average error in rating, but a bit different -- it's the square root of average squared error. But if you think of the ratings as 'stars' you could kind of think of 0.86 as 'generally off by 0.86' stars and that would be somewhat right.
Whether that's good depends on what the range of input was. For 1-5 that's OK; for 1-100 it would be fantastic. To give you a point of comparison, when Netflix launched their Netflix Prize, their recommender had an RMSE of 0.95 or so. The winning solution was at about 0.85. Their data set was a larger, harder problem than the movielens data set though. So: reasonably good. On Tue, Feb 24, 2015 at 8:19 PM, Krishna Sankar <ksanka...@gmail.com> wrote: > Yep, much better with 0.1. > > "The best model was trained with rank = 12 and lambda = 0.1, and numIter = > 20, and its RMSE on the test set is 0.869092" (Spark 1.3.0) > > Question : What is the intuition behind RSME of 0.86 vs 1.3 ? I know the > smaller the better. But is it that better ? And what is a good number for a > recommendation engine ? > > Cheers > <k/> --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
