No, that's certainly not to be expected. ALS works by computing a much lower-rank representation of the input. It would not reproduce the input exactly, and you don't want it to -- this would be seriously overfit. This is why in general you don't evaluate a model on the training set.
On Sat, Jul 23, 2016 at 7:37 PM, VG <vlin...@gmail.com> wrote: > I am trying to run ml.ALS to compute some recommendations. > > Just to test I am using the same dataset for training using ALSModel and for > predicting the results based on the model . > > When I evaluate the result using RegressionEvaluator I get a > Root-mean-square error = 1.5544064263236066 > > I thin this should be 0. Any suggestions what might be going wrong. > > Regards, > Vipul --------------------------------------------------------------------- To unsubscribe e-mail: user-unsubscr...@spark.apache.org
