HyperTab is a hypernetwork-based classifier for small tabular datasets.
It's especially efficient when the number of samples is smaller than 500. The smaller the dataset, the larger is the advantage of HyperTab over other algorithms.
pip install hypertabfrom hypertab import HyperTabClassifier
DEVICE = "cuda:0" if torch.cuda.is_available() else "cpu"
clf = HyperTabClassifier(0.5, device=DEVICE, test_nodes=100, epochs=10, hidden_dims=5)
clf.fit(X, y)
clf.predict(X)| Dataset | XGBoost | DN | RF | HyperTab | Node |
|---|---|---|---|---|---|
| Wisconsin Breast Cancer | 93.85 | 95.58 | 95.96 | 97.58 | 96.19 |
| Connectionist Bench | 83.52 | 79.02 | 83.50 | 87.09 | 85.61 |
| Dermatology | 96.05 | 97.80 | 97.21 | 97.82 | 97.99 |
| Glass | 94.74 | 46.96 | 97.02 | 98.36 | 44.90 |
| Promoter | 81.88 | 78.91 | 85.94 | 89.06 | 83.75 |
| Ionosphere | 90.67 | 93.43 | 92.43 | 94.52 | 91.03 |
| Libras | 74.38 | 81.54 | 77.42 | 85.22 | 82.72 |
| Lymphography | 85.94 | 85.74 | 87.19 | 83.90 | 83.93 |
| Parkinsons | 86.35 | 74.96 | 86.84 | 95.27 | 80.20 |
| Zoo | 92.86 | 72.62 | 92.62 | 95.27 | 89.05 |
| Hill-Valley without noise | 65.53 | 56.39 | 57.33 | 70.59 | 52.71 |
| Hill-Valley with noise | 58.45 | 56.06 | 55.66 | 67.56 | 51.09 |
| OpenML 1086 | 60.61 | 33.33 | 51.24 | 76.60 | 68.39 |
| Heart | 79.17 | 82.62 | 81.10 | 83.33 | 82.38 |
| Mean rank | 3.50 | 3.78 | 3.07 | 1.35 | 3.29 |