Model | Accuracy | AUC | Recall | Prec. | F1 | Kappa | MCC | TT (Sec) |
---|---|---|---|---|---|---|---|---|
Extreme Gradient Boosting | 0.9554 | 0.698 | 0.8012 | 0.9569 | 0.9545 | 0.943 | 0.9434 | 298.3 |
CatBoost Classifier | 0.9476 | 0.6974 | 0.7398 | 0.9503 | 0.9461 | 0.9329 | 0.9335 | 49.64 |
Extra Trees Classifier | 0.9457 | 0.6949 | 0.7547 | 0.9453 | 0.9447 | 0.9306 | 0.9308 | 15.27 |
Logistic Regression | 0.9427 | 0.6969 | 0.7258 | 0.9458 | 0.9409 | 0.9267 | 0.9275 | 41.51 |
Gradient Boosting Classifier | 0.9402 | 0.6944 | 0.7518 | 0.9446 | 0.939 | 0.9234 | 0.9243 | 170.4 |
SVM - Linear Kernel | 0.9396 | 0 | 0.6948 | 0.9446 | 0.937 | 0.9226 | 0.9237 | 3.268 |
Ridge Classifier | 0.937 | 0 | 0.6917 | 0.9437 | 0.9342 | 0.9191 | 0.9203 | 0.396 |
Linear Discriminant Analysis | 0.934 | 0.6953 | 0.6867 | 0.9444 | 0.9333 | 0.9156 | 0.9175 | 8.38 |
K Neighbors Classifier | 0.8455 | 0.6678 | 0.4984 | 0.8341 | 0.8366 | 0.8014 | 0.802 | 121.1 |
Naive Bayes | 0.818 | 0.6401 | 0.7371 | 0.952 | 0.8615 | 0.7776 | 0.7866 | 1.359 |
Ada Boost Classifier | 0.7951 | 0.5463 | 0.4443 | 0.6992 | 0.7353 | 0.7255 | 0.7456 | 3.36 |
Light Gradient Boosting Machine | 0.6293 | 0.5559 | 0.3534 | 0.6996 | 0.6429 | 0.5442 | 0.5484 | 20.12 |
Quadratic Discriminant Analysis | 0.0111 | 0.354 | 0.0496 | 0.2889 | 0.0203 | 0.0068 | 0.031 | 4.846 |