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Average classification accuracy of different algorithms for identifying mood disorders.
| Algorithms | Accuracy |
|---|---|
| K-nearest neighbor | 89.44% |
| Logistic regression | 86.50% |
| LASSO | 88.41% |
| Elastic net | 77.65% |
| Ridge regression | 75.75% |
| Decision tree | 84.96% |
| Random forests | 89.79% |
| SVM with all features | 89.28% |
| SVM with selected features | 90.60% |
LASSO, least absolute shrinkage and selection operator; SVM, support vector machine.
