Kernel | Category | ACC | SE | SP | MCC |
---|
Linear | Training | 80.7 | 90.4 | 64.8 | 0.581 |
 | Test | 68.8 | 82.1 | 46.6 | 0.312 |
 | External | 70.7 | 77.6 | 59.1 | 0.375 |
Polynomial | Training | 79.2 | 96.4 | 50.7 | 0.506 |
 | Test | 65.8 | 86.8 | 30.8 | 0.198 |
 | External | 66.1 | 81.9 | 39.8 | 0.174 |
RBF | Training | 84.5 | 93.8 | 69.1 | 0.665 |
 | Test | 69.7 | 83.3 | 47.2 | 0.332 |
 | External | 70.9 | 77.7 | 59.6 | 0.382 |
- ACC, accuracy (overall prediction accuracy); SP, specificity (prediction accuracy for the non-substrates); SE, sensitivity (prediction accuracy for the substrates); MCC, the Matthews correlation coefficient (a more balanced prediction parameter than ACC). The external data set was only used to validate the prediction power of the models constructed, and was not used for model selection.