I did not change anything from the example provided in mahout-example, development version. It uses 5% for evaluation, which is 5000 instances. With such test set size, the range should not be that big. I suspect that there is something wrong somewhere.
Selon Sean Owen <[email protected]>: > It depends on how big a subset of the data you are using to evaluate, > and also how much you are using for test versus training. Yes, that > kind of range is undesirable. How are you executing the evaluation? > > On Tue, Mar 9, 2010 at 4:50 PM, <[email protected]> wrote: > > Hello, > > > > When testing the mahout example BookCrossingRecommender with default > settings > > (GenericUserBasedRecommender, PearsonCorrelationSimilarity, > > NearestNUserNeighborhood), I noticed that the result of the evaluation > > (AverageAbsoluteDifferenceRecommenderEvaluator) are > > changing randomly, from one test to another. I get scores between 2.1 and > 4.8. > > > > Considering the size of the input (about 100000 users and 100000 books), I > can't > > imagine that the randomness in the algorithms can lead to huge evaluation > > differences like that. > > > > What do you think? > > >
