Table 3.
Learning algorithms and their default settings.
| Classifier | Description | Notes |
|---|---|---|
| DT | CART decision tree | minimum 1 instance per leaf |
| SVM Poly | Support Vector Machine | polynomial kernel of degree 3 |
| SVM RBF | Support Vector Machine | RBF kernel, gamma = 0.0 |
| LogReg | Logistic Regression | L2 regularization |
| kNN | k nearest neighbors | k = 5 |
| AdaBoost | Adaptive boosting | Decision trees, 50 base estimators |
| Bagging | Bagging using CART tree | 10 base estimators |
| NB | Naïve Bayes | |
| RF | Random forest | 500 trees, inspected features = |