Yes, you overfit the training data set, so you "under-fit" the test
set. I'm trying to suggest why more degrees of freedom (features)
makes for a "worse" fit. It doesn't, on the training set, but those
same parameters may fit the test set increasingly badly.
It doesn't make sense to evaluate on a
Yes, but overfitting is for train dataset isn't it? However, now I'm
evaluating on a test dataset (which is sampled from the whole dataset, but
that still makes it test), so don't really understand how can overfitting
become an issue. :-?
Is there any class/function to make the evaluation on the t
OK I keep thinking ALS-WR = weighted terms / implicit feedback but
that's not the case here it seems.
Well scratch that part, but I think the answer is still overfitting.
On Thu, May 9, 2013 at 2:45 PM, Gabor Bernat wrote:
> I've used the constructor without that argument (or alpha). So I suppose
I've used the constructor without that argument (or alpha). So I suppose
those take the default value, which I suppose is an explicit model, am I
right?
Thanks,
Bernát GÁBOR
On Thu, May 9, 2013 at 3:40 PM, Sebastian Schelter
wrote:
> Our ALSWRFactorizer can do both flavors of ALS (the one used
Our ALSWRFactorizer can do both flavors of ALS (the one used for
explicit and the one used for implicit data). @Gabor, what do you
specify for the constructor argument "usesImplicitFeedback" ?
On 09.05.2013 15:33, Sean Owen wrote:
> RMSE would have the same potential issue. ALS-WR is going to pre
RMSE would have the same potential issue. ALS-WR is going to prefer to
minimize one error at the expense of letting another get much larger,
whereas RMSE penalizes them all the same. It's maybe an indirect
issue here at best -- there's a moderate mismatch between the metric
and the nature of the a
I know, but the same is true for the RMSE.
This is based on the Movielens 100k dataset, and by using the frameworks
(random) sampling to split that into a training and an evaluation set. (the
RMSRecommenderEvaluator or AverageAbsoluteDifferenceRecommenderEvaluators
paramters - evaluation 1.0, trai
(The MAE metric may also be a complicating issue... it's measuring
average error where all elements are equally weighted, but as the "WR"
suggests in ALS-WR, the loss function being minimized weights
different elements differently.)
This is based on a test set right, separate from the training set
Hello,
Here it is: http://i.imgur.com/3e1eTE5.png
I've used 75% for training and 25% for evaluation.
Well reasonably lambda gives close enough results, however not better.
Thanks,
Bernát GÁBOR
On Thu, May 9, 2013 at 2:46 PM, Sean Owen wrote:
> This sounds like overfitting. More features le
This sounds like overfitting. More features lets you fit your training
set better, but at some point, fitting too well means you fit other
test data less well. Lambda resists overfitting, so setting it too low
increases the overfitting problem.
I assume you still get better test set results with a
Gabor,
attachments are not allowed on this list, you have to upload the picture
somewhere and provide a link to it.
Best,
Sebastian
On 09.05.2013 14:38, Gabor Bernat wrote:
> Hello,
>
> So I've been testing out the ALSWR with the Movielensk 100k dataset, and
> I've run in some strange stuff. An
Hello,
So I've been testing out the ALSWR with the Movielensk 100k dataset, and
I've run in some strange stuff. An example of this you can see in the
attached picture.
So I've used feature count1,2,4,8,16,32, same for iteration and summed up
the results in a table. So for a lambda higher than 0.0
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