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Tabular Data Generation On Sick
Metrics
DT Accuracy
LR Accuracy
Parameters(M)
RF Accuracy
Results
Performance results of various models on this benchmark
| Paper Title | Repository | |||||
|---|---|---|---|---|---|---|
| GReaT | 97.72 | 97.72 | 355 | 98.3 | Language Models are Realistic Tabular Data Generators | |
| Binary Diffusion | 97.07 | 96.14 | 1.4 | 96.59 | Tabular Data Generation using Binary Diffusion | |
| TVAE | 95.39 | 94.7 | 0.046 | 94.91 | Modeling Tabular data using Conditional GAN | |
| Distill-GReaT | 95.39 | 96.56 | 82 | 97.72 | Language Models are Realistic Tabular Data Generators | |
| CopulaGAN | 93.77 | 94.57 | 0.226 | 94.57 | Modeling Tabular data using Conditional GAN | |
| CTGAN | 92.05 | 94.44 | 0.222 | 94.57 | Modeling Tabular data using Conditional GAN |
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