How are you computing RMSE? and how are you training the model -- not with trainImplicit right? I wonder if you are somehow optimizing something besides RMSE.
On Wed, Nov 26, 2014 at 2:36 PM, Kostas Kloudas <kklou...@gmail.com> wrote: > Once again, the error even with the training dataset increases. The results > are: > > Running 1 iterations > For 1 iter.: Test RMSE = 1.2447121194304893 Training RMSE = > 1.2394166987104076 (34.751317636 s). > Running 5 iterations > For 5 iter.: Test RMSE = 1.3253957117600659 Training RMSE = > 1.3206317416138509 (37.693118023000004 s). > Running 9 iterations > For 9 iter.: Test RMSE = 1.3255293380139364 Training RMSE = > 1.3207661218210436 (41.046175661 s). > Running 13 iterations > For 13 iter.: Test RMSE = 1.3255295352665748 Training RMSE = > 1.3207663201865092 (47.763619515 s). > Running 17 iterations > For 17 iter.: Test RMSE = 1.32552953555787 Training RMSE = > 1.3207663204794406 (59.682361103000005 s). > Running 21 iterations > For 21 iter.: Test RMSE = 1.3255295355583026 Training RMSE = > 1.3207663204798756 (57.210578232 s). > Running 25 iterations > For 25 iter.: Test RMSE = 1.325529535558303 Training RMSE = > 1.3207663204798765 (65.785485882 s). > > Thanks a lot, > Kostas > > On Nov 26, 2014, at 12:04 PM, Nick Pentreath <nick.pentre...@gmail.com> > wrote: > > copying user group - I keep replying directly vs reply all :) > > On Wed, Nov 26, 2014 at 2:03 PM, Nick Pentreath <nick.pentre...@gmail.com> > wrote: >> >> ALS will be guaranteed to decrease the squared error (therefore RMSE) in >> each iteration, on the training set. >> >> This does not hold for the test set / cross validation. You would expect >> the test set RMSE to stabilise as iterations increase, since the algorithm >> converges - but not necessarily to decrease. >> >> On Wed, Nov 26, 2014 at 1:57 PM, Kostas Kloudas <kklou...@gmail.com> >> wrote: >>> >>> Hi all, >>> >>> I am getting familiarized with Mllib and a thing I noticed is that >>> running the MovieLensALS >>> example on the movieLens dataset for increasing number of iterations does >>> not decrease the >>> rmse. >>> >>> The results for 0.6% training set and 0.4% test are below. For training >>> set to 0.8%, the results >>> are almost identical. Shouldn’t it be normal to see a decreasing error? >>> Especially going from 1 to 5 iterations. >>> >>> Running 1 iterations >>> Test RMSE for 1 iter. = 1.2452964343277886 (52.757125927000004 s). >>> Running 5 iterations >>> Test RMSE for 5 iter. = 1.3258973764470259 (61.183927666 s). >>> Running 9 iterations >>> Test RMSE for 9 iter. = 1.3260308117704385 (61.84948875800001 s). >>> Running 13 iterations >>> Test RMSE for 13 iter. = 1.3260310099809915 (73.799510125 s). >>> Running 17 iterations >>> Test RMSE for 17 iter. = 1.3260310102735398 (77.56512185300001 s). >>> Running 21 iterations >>> Test RMSE for 21 iter. = 1.3260310102739703 (79.607495074 s). >>> Running 25 iterations >>> Test RMSE for 25 iter. = 1.326031010273971 (88.631776301 s). >>> Running 29 iterations >>> Test RMSE for 29 iter. = 1.3260310102739712 (101.178383079 s). >>> >>> Thanks a lot, >>> Kostas >>> --------------------------------------------------------------------- >>> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org >>> For additional commands, e-mail: user-h...@spark.apache.org >>> >> > > --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
