Tabular Prediction on GE
0.7ScoreMLP-PLR
Evaluation results
| Method | Links | |
|---|---|---|
| MLP-PLRBackbone=MLP, Numerical Feature Embedding=Piecewise Linear2022.03 | 0.7 | |
| CatBoostBackbone=GBDT2022.03 | 0.692 | |
| ResNet-PLRBackbone=ResNet, Numerical Feature Embedding=Piecewise Linear2022.03 | 0.691 | |
| ResNetBackbone=ResNet2022.03 | 0.69 | |
| Transformer-Q-LRBackbone=Transformer, Numerical Feature Embedding=Quantile2022.03 | 0.69 | |
| Transformer-T-LRBackbone=Transformer, Numerical Feature Embedding=Periodic2022.03 | 0.686 | |
| Transformer-PLRBackbone=Transformer, Numerical Feature Embedding=Piecewise Linear2022.03 | 0.686 | |
| XGBoostBackbone=GBDT2022.03 | 0.683 | |
| ResNet-T-LRBackbone=ResNet, Numerical Feature Embedding=Periodic2022.03 | 0.683 | |
| MLP-Q-LRBackbone=MLP, Numerical Feature Embedding=Quantile2022.03 | 0.682 | |
| MLP-LRBackbone=MLP, Numerical Feature Embedding=Linear2022.03 | 0.679 | |
| ResNet-Q-LRBackbone=ResNet, Numerical Feature Embedding=Quantile2022.03 | 0.674 | |
| MLP-T-LRBackbone=MLP, Numerical Feature Embedding=Periodic2022.03 | 0.673 | |
| ResNet-LRBackbone=ResNet, Numerical Feature Embedding=Linear2022.03 | 0.672 | |
| Transformer-LBackbone=Transformer2022.03 | 0.668 | |
| Transformer-LRBackbone=Transformer, Numerical Feature Embedding=Linear2022.03 | 0.666 | |
| MLPBackbone=MLP2022.03 | 0.665 |