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 reasonable lambda? On Thu, May 9, 2013 at 1:38 PM, Gabor Bernat <ber...@primeranks.net> wrote: > 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 0.07 the more important > factor seems to be the iteration count, while increasing the feature count > may improve the result, however not that much. And this is what one could > expect from the algrithm, so that's okay. > > The strange stuff comes for lambdas smaller than 0.075. In this case the > more important part becames the feature count, hovewer not more but less is > better. Similary for the iteration count. Essentially the best score is > achieved for a really small lambda, and a single feature and iteration > count. How is this possible, am I missing something? > > > Bernát GÁBOR
