Tabular Data Generation on HELOC
DT Accuracy
- 2019-07-01 — TVAE: DT Accuracy 76.39
- 2022-10-12 — Distill-GReaT: DT Accuracy 81.4
| Rank | Model | DT Accuracy | LR Accuracy | Parameters(M) | RF Accuracy | Paper | Code | Year |
|---|---|---|---|---|---|---|---|---|
| 1 | Distill-GReaT | 81.4 | 70.58 | 82 | 82.14 | Language Models are Realistic Tabular Data Generators | kathrinse/be_great | 2022 |
| 2 | GReaT | 79.1 | 71.9 | 355 | 80.93 | Language Models are Realistic Tabular Data Generators | kathrinse/be_great | 2022 |
| 3 | TVAE | 76.39 | 71.04 | 62 | 77.24 | Modeling Tabular data using Conditional GAN | ydataai/ydata-synthetic · DAI-Lab/CTGAN · sdv-dev/CTGAN · +6 | 2019 |
| 4 | Binary Diffusion | 70.25 | 71.76 | 2.6 | 70.47 | Tabular Data Generation using Binary Diffusion | vkinakh/binary-diffusion-tabular | 2024 |
| 5 | CTGAN | 61.34 | 57.72 | 0.277 | 62.35 | Modeling Tabular data using Conditional GAN | ydataai/ydata-synthetic · DAI-Lab/CTGAN · sdv-dev/CTGAN · +6 | 2019 |
| 6 | CopulaGAN | 42.36 | 42.03 | 0.276 | 42.35 | Modeling Tabular data using Conditional GAN | ydataai/ydata-synthetic · DAI-Lab/CTGAN · sdv-dev/CTGAN · +6 | 2019 |