Hi! when you usefxml.py delicious.model deliciousLarge_train.txt --standard-dataset --verbose train --iters 5 --trees 20 --label-weight propensity --alpha 1e-4 --leaf-classifiers --no-remap-labels to train and use fxml.py delicious.model deliciousLarge_test.txt --standard-dataset inference to test,
what is the final result?
I get very low score, like that:
P@1: 0.4287878787878788 P@3: 0.38484848484848483 P@5: 0.3565656565656566 NDCG@1: 0.4287878787878788 NDCG@3: 0.3957641604407556 NDCG@5: 0.3747889067931902 pNDCG@1: 0.45656907373737377 pNDCG@3: 0.41984043916568803 pNDCG@5: 0.396182084153063
Hi! when you use
fxml.py delicious.model deliciousLarge_train.txt --standard-dataset --verbose train --iters 5 --trees 20 --label-weight propensity --alpha 1e-4 --leaf-classifiers --no-remap-labelsto train and usefxml.py delicious.model deliciousLarge_test.txt --standard-dataset inferenceto test,what is the final result? I get very low score, like that:
P@1: 0.4287878787878788 P@3: 0.38484848484848483 P@5: 0.3565656565656566 NDCG@1: 0.4287878787878788 NDCG@3: 0.3957641604407556 NDCG@5: 0.3747889067931902 pNDCG@1: 0.45656907373737377 pNDCG@3: 0.41984043916568803 pNDCG@5: 0.396182084153063