From: Improving binary classification using filtering based on k-NN proximity graphs
Classifier | Decision Tree | Logistic regression | Naive Bayes | SVM | Neural Network | Random Forest | DES-LA |
---|---|---|---|---|---|---|---|
Accuracy | 0.813 | 0.818 | 0.79 | 0.815 | 0.794 | 0.817 | 0.835 |
Sensitivity | 0.951 | 0.958 | 0.875 | 0.956 | 0.985 | 0.954 | 0.95 |
Specificity | 0.327 | 0.324 | 0.491 | 0.322 | 0.124 | 0.334 | 0.365 |
AUC | 0.688 | 0.713 | 0.712 | 0.714 | 0.683 | 0.729 | 0.769 |
Brier Score | 0.179 | 0.162 | 0.192 | 0.15 | 0.17 | 0.161 | 0.159 |