Table 2.
Number of features in the models.
| Dataset | AM | RO |
|---|---|---|
| Method | (n = 151) | (n = 286) |
|
| ||
| RF | 21 | 21 |
| LR | 5 | 11 |
| R-SVM | 20 | 25 |
| L-SVM | 8 | 11 |
| P-SVM | 4 | 7 |
| S-SVM | 17 | 35 |
| NNET | 21 | 16 |
AM and RO are the Amsterdam and Rotterdam training sets and n shows the number of samples in the respective datasets. Methods used are as follows: RF: random forest, LR: logistic regression, R-, L-, P-, and S-SVM: support vector machine with a radial basis function, linear, polynomial, or sigmoid kernel, and NNET: neural network with a single hidden layer.