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 <[email protected]> wrote: > 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 <[email protected]> > 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 >
