Table 3.
Leaderboard for AutoGluon listing the best performing individual classification models from the ensemble model. The attributes Score_test and Score_val are log-loss used to evaluate predictive performance, and the models were sorted according to performance. Note that the closer the value is to zero, the better the model. For details on the other attributes, Stack_level and Fit_order, refer to [29].
| Ranking | Model | Score_Test | Score_Val | Stack_Level | Fit_Order |
|---|---|---|---|---|---|
| 1 | CatboostClassifier | −0.196 | −0.299 | 1 | 22 |
| 2 | LightGBMClassifierXT | −0.200 | −0.293 | 1 | 21 |
| 3 | weighted_ensemble | −0.211 | −0.269 | 2 | 24 |
| 4 | LightGBMClassifierCustom | −0.214 | −0.345 | 1 | 23 |
| 5 | LightGBMClassifier | −0.217 | −0.318 | 1 | 20 |
| 6 | RandomForestClassifierEntr | −0.223 | −0.304 | 1 | 17 |
| 7 | ExtraTreesClassifierGini | −0.228 | −0.272 | 1 | 18 |
| 8 | ExtraTreesClassifierEntr | −0.231 | −0.281 | 1 | 19 |
| 9 | weighted_ensemble | −0.236 | −0.319 | 1 | 12 |
| 10 | ExtraTreesClassifierEntr | −0.246 | −0.388 | 0 | 7 |
| 11 | ExtraTreesClassifierGini | −0.249 | −0.380 | 0 | 6 |
| 12 | CatboostClassifier | −0.254 | −0.354 | 0 | 10 |
| 13 | LightGBMClassifierXT | −0.254 | −0.347 | 0 | 9 |
| 14 | LightGBMClassifier | −0.270 | −0.369 | 0 | 8 |
| 15 | LightGBMClassifierCustom | −0.276 | −0.396 | 0 | 11 |
| 16 | RandomForestClassifierGini | −0.278 | −0.305 | 1 | 16 |
| 17 | NeuralNetClassifier | −0.303 | −0.416 | 0 | 1 |
| 18 | RandomForestClassifierEntr | −0.311 | −0.374 | 0 | 5 |
| 19 | NeuralNetClassifier | −0.313 | −0.421 | 1 | 13 |
| 20 | RandomForestClassifierGini | −0.318 | −0.381 | 0 | 4 |
| 21 | KNeighborsClassifierDist | −1.058 | −1.625 | 1 | 15 |
| 22 | KNeighborsClassifierDist | −1.074 | −1.757 | 0 | 3 |
| 23 | KNeighborsClassifierUnif | −1.227 | −1.767 | 1 | 14 |
| 24 | KNeighborsClassifierUnif | −1.269 | −1.901 | 0 | 2 |