> - Is there a way to specify the train and test set like you can with the > *RecommenderEvaluator*? No, though you can specify the evaluation percentage. This is because of the logic of evaluation. The logic is to take away relevant items and then make recommendations and see whether the relevant items appear in top-N lists. It is also possible (and I think in some ways better) to first split the data into test and training and select relevant items from the test set. But this is not how it is implemented.
> - Is it possible to perform k-fold cross-validation with the > *RecommenderIRStatsEvaluator*? I don't think so. > - How does the default way of evaluation work with > *RecommenderIRStatsEvaluator*? I tried to explain it above. I would like to remind that it is not difficult to write your own evaluation code for your specific purposes. Tevfik On Tue, May 20, 2014 at 3:51 PM, Floris Devriendt <[email protected]> wrote: > Hey all, > > The *RecommenderEvaluator *has the option to choose how big your training > set is (and so choosing the test set size as well), but the > *RecommenderIRStatsEvaluator* does not seem to have this argument in its > *.evaluate()*-method. That's why I was wondering how the internals of the > *RecommenderIRStatsEvaluator* work. > > I have the following questions on *RecommenderIRStatsEvaluator*: > > - Is there a way to specify the train and test set like you can with the > *RecommenderEvaluator*? > - Is it possible to perform k-fold cross-validation with the > *RecommenderIRStatsEvaluator*? > - How does the default way of evaluation work with > *RecommenderIRStatsEvaluator*? > > If somebody has an answer to any of these questions it would be greatly > appreciated. > > Kind regards, > Floris Devriendt
