Table 2.
Mean performance for the ML algorithms trained on the Atrain dataset, evaluated on Atest
| Models | Atest dataset | |||
|---|---|---|---|---|
| Recall | Precision | |||
| M | SD | M | SD | |
| Bernoulli | 0.01172 | 0.00079 | 0.01139 | 0.00096 |
| CT | 0.9841 | 0.016 | 0.9779 | 0.0059 |
| Bagged trees | 0.9809 | 0.012 | 0.9826 | 0.0080 |
| AdaBoost | 0.9839 | 0.011 | 0.9828 | 0.0075 |
| RF | 0.9853 | 0.011 | 0.9824 | 0.010 |
| SVM | 0.9821 | 0.017 | 0.9789 | 0.0068 |
| NNET | 0.9823 | 0.012 | 0.9843 | 0.0078 |
Six machine learning algorithms were tested: Classification Tree (CT), Bagged trees, AdaBoost, Random Forest (RF), Support Vector Machine (SVM) and Neural Network (NNET). M mean, SD standard deviation. The highest mean values among the different algorithms are highlighted in bold