The following repository has a few tested baselines for tabular datasets outside of the two fastai uses (Rossmann and ADULTs).
Along with these include a few popular techniques also used, and how fastai compares.
| Model | Test Accuracy (%) |
|---|---|
| Decision Tree | 50% |
| Multi-layer perceptron | 50% |
| Deep Neural Decision Tree | 65.1% |
| TabNet | 99.3% |
| fastai2 | 99.44% |
Credit to Fabio Barros for the idea of treating the numerical cards as both categorical and continuous.
Sarcos Robotics Arm Inverse Dynamics
| Model | MSE | Number of Parameters |
|---|---|---|
| Random Forest | 2.39 | 16.7K |
| Stochastic Decision Tree | 2.11 | 28K |
| Multi-Layer Perceptron | 2.13 | 0.14M |
| Adaptive Neural Tree Ensemble | 1.23 | 0.60M |
| Gradient Boosted Tree | 1.44 | 0.99M |
| TabNet-S | 1.25 | 6.3K |
| TabNet-M | 0.28 | 590K |
| TabNet-L | 0.14 | 1.75M |
| fastai2 |
| Model | Test Accuracy (%) | Number of Parameters |
|---|---|---|
| Sparse evolutionary trained multi-layer perceptron | 78.47 | 81K |
| Gradient boosted tree - S | 74.22 | 120K |
| Gradient boosted tree - M | 75.97 | 690K |
| Multi-layer perceptron | 78.44 | 2.04M |
| Gradient boosted tree - L | 76.98 | 6.96M |
| TabNet - S | 78.25 | 81K |
| TabNet - M | 78.84 | 660K |
| fastai2 | 76.94 | 530K |