Recidivism prediction on NLSY97
0.6559AUCExtra Trees Classifier
Evaluation results
| Method | Links | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Extra Trees ClassifierTT (s)=0.45702026.01 | 0.6559 | 75.51 | 75.51 | 71.07 | — | 70.25 | 0.1249 | 0.1438 | |
| CatBoost ClassifierTT (s)=6.58102026.01 | 0.6449 | 74.6 | 74.6 | 71.02 | — | 70.08 | 0.1295 | 0.1433 | |
| Random Forest ClassifierTT (s)=0.51102026.01 | 0.6448 | 76.06 | 76.06 | 70.2 | — | 69.29 | 0.0939 | 0.1142 | |
| TabPFN baselinelearning_rate=0.01, max_depth=3, n_estimators=1002026.01 | 0.641 | 70.11 | 0 | 0 | — | — | — | — | |
| Gradient Boosting ClassifierTT (s)=0.75002026.01 | 0.6375 | 74.61 | 74.61 | 71.9 | — | 71.35 | 0.1695 | 0.1816 | |
| Ridge ClassifierTT (s)=0.37502026.01 | 0.612 | 61.87 | 61.87 | 64.42 | — | 70.8 | 0.1403 | 0.158 | |
| Linear Discriminant AnalysisTT (s)=0.26702026.01 | 0.6103 | 61.31 | 61.31 | 63.93 | — | 70.64 | 0.1324 | 0.1512 | |
| Logistic RegressionTT (s)=0.53702026.01 | 0.6088 | 62.98 | 62.98 | 65.38 | — | 70.98 | 0.1524 | 0.1672 | |
| Extreme Gradient BoostingTT (s)=0.67502026.01 | 0.6015 | 73.68 | 73.68 | 70.24 | — | 69.19 | 0.1091 | 0.1211 | |
| AdaBoost ClassifierTT (s)=0.45502026.01 | 0.597 | 70.36 | 70.36 | 68.81 | — | 67.91 | 0.1016 | 0.0997 | |
| Naive BayesTT (s)=0.26802026.01 | 0.5854 | 29.1 | 29.1 | 23.28 | — | 64.25 | -0.0029 | -0.0069 | |
| Decision Tree ClassifierTT (s)=0.27002026.01 | 0.5299 | 64.45 | 64.45 | 65.27 | — | 66.38 | 0.0554 | 0.056 | |
| SVM (Linear Kernel)TT (s)=0.28202026.01 | 0.5231 | 34.31 | 34.31 | 21.89 | — | 29.08 | -0.0013 | 0.0014 | |
| Quadratic Discriminant AnalysisTT (s)=0.27102026.01 | 0.5167 | 27.63 | 27.63 | 20.3 | — | 62.67 | -0.0065 | -0.019 | |
| K Neighbors ClassifierTT (s)=0.45802026.01 | 0.4789 | 47.89 | 47.89 | 51.53 | — | 63.5 | -0.0144 | -0.0186 |