Wide & Deep (LR & DNN)
Reported on 2 benchmarks across 1 task · 1 paper
Note: results are matched by exact model name. Different papers may use the same name for different model variants.
Miscellaneous2 results
- AUC· 2016-06-240.8673best: 0.8715 (DeepFM)
- Log Loss· 2016-06-240.02634best: 0.02618 (DeepFM)