Tabular Classification on Sixteen tabular classification data sets
20.218AccuracySVM
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| SVM2026.05 | 20.218 | 18.844 | 20.156 | 18.656 | |
| UCB1 pruning2026.05 | 19.063 | 18.625 | 16.5 | 18.063 | |
| Unpruned NN2026.05 | 18.531 | 17.843 | 15.593 | 17.219 | |
| Gaussian process2026.05 | 16.906 | 17.344 | 16.781 | 17.219 | |
| Thompson Sampling pruning2026.05 | 16.313 | 16.031 | 13.781 | 15.531 | |
| Decay Epsilon-Greedy pruning2026.05 | 15.719 | 14.531 | 15.094 | 14.313 | |
| Epsilon-Greedy pruning2026.05 | 15.625 | 15.063 | 14.188 | 14.625 | |
| Softmax pruning2026.05 | 15.156 | 13.969 | 14.625 | 13.719 | |
| Decay Softmax pruning2026.05 | 14.969 | 14.531 | 16.625 | 12.719 | |
| Random forest2026.05 | 14.5 | 14.031 | 16.594 | 12.75 | |
| Hedge-style MW pruning2026.05 | 14.406 | 13.531 | 13.938 | 13.031 | |
| KNN2026.05 | 14.156 | 14.438 | 13.125 | 14.312 | |
| LightGBM2026.05 | 14.094 | 12.25 | 12.063 | 10.906 | |
| XGBoost2026.05 | 13.813 | 12.469 | 15.188 | 11.938 | |
| Bagging KNN2026.05 | 13.188 | 11.5 | 13 | 11.031 | |
| EXP3 pruning2026.05 | 12.281 | 12.344 | 12.094 | 11.563 | |
| Decision treesplit criterion=entropy2026.05 | 11.156 | 13.563 | 10.906 | 14.469 | |
| Bagging decision tree2026.05 | 10.407 | 9.719 | 11.906 | 8.125 | |
| Decision treesplit criterion=Gini2026.05 | 9.875 | 13.656 | 10.781 | 16.063 | |
| QDA2026.05 | 8.844 | 12.25 | 9.281 | 13.031 | |
| Logistic regression2026.05 | 8.781 | 7.594 | 10.781 | 8.406 | |
| LDA2026.05 | 8.313 | 8.656 | 9.813 | 8.313 | |
| SVMkernel=linear2026.05 | 7.594 | 7.438 | 8.594 | 8.875 | |
| AdaBoost2026.05 | 6.907 | 8.031 | 8.094 | 10.406 | |
| Naive Bayes2026.05 | 4.188 | 7.594 | 5.406 | 9.719 |