Tabular Data Generation on Diabetes
DT Accuracy
- 2019-07-01 — TVAE: DT Accuracy 0.533
- 2022-10-12 — GReaT: DT Accuracy 0.5523
- 2024-09-20 — Binary Diffusion: DT Accuracy 0.5713
| Rank | Model | DT Accuracy | Parameters(M) | LR Accuracy | RF Accuracy | Paper | Code | Year |
|---|---|---|---|---|---|---|---|---|
| 1 | Binary Diffusion | 0.5713 | 1.8 | 0.5775 | 0.5752 | Tabular Data Generation using Binary Diffusion | vkinakh/binary-diffusion-tabular | 2024 |
| 2 | GReaT | 0.5523 | 355 | 0.5734 | 0.5834 | Language Models are Realistic Tabular Data Generators | kathrinse/be_great | 2022 |
| 3 | Distill-GReaT | 0.541 | 82 | 0.5733 | 0.5803 | Language Models are Realistic Tabular Data Generators | kathrinse/be_great | 2022 |
| 4 | TVAE | 0.5330 | 0.359 | 0.5634 | 0.5517 | Modeling Tabular data using Conditional GAN | ydataai/ydata-synthetic · DAI-Lab/CTGAN · sdv-dev/CTGAN · +6 | 2019 |
| 5 | CTGAN | 0.4973 | 9.6 | 0.5093 | 0.5223 | Modeling Tabular data using Conditional GAN | ydataai/ydata-synthetic · DAI-Lab/CTGAN · sdv-dev/CTGAN · +6 | 2019 |
| 6 | CopulaGAN | 0.385 | 9.4 | 0.4027 | 0.3759 | Modeling Tabular data using Conditional GAN | ydataai/ydata-synthetic · DAI-Lab/CTGAN · sdv-dev/CTGAN · +6 | 2019 |