Table 3.
Prediction performance of the hub classifier in the combined data set of the four species
| Hub classifier (# of nodes in each tree = 15, FN: FP penalty = 1:1.9, total # of trees = 187) | |||||||
| Training | |||||||
| observed | predicted non-hub | predicted hub | sensitivity | specificity | accuracy | PPV | NPV |
| non-hub | 13381 | 1405 | 36.51% | 90.50% | 85.37% | 28.75% | 93.14% |
| hub | 986 | 567 | |||||
| Testing | |||||||
| observed | predicted non-hub | predicted hub | sensitivity | specificity | accuracy | PPV | NPV |
| non-hub | 4415 | 514 | 28.10% | 89.57% | 83.75% | 22.00% | 92.25% |
| hub | 371 | 145 | |||||
| All | |||||||
| observed | predicted non-hub | predicted hub | sensitivity | specificity | accuracy | PPV | NPV |
| non-hub | 17796 | 1919 | 34.41% | 90.27% | 84.96% | 27.06% | 92.91% |
| hub | 1357 | 712 | |||||
The observed vs. predicted hubs and non-hubs and their corresponding classification statistics are shown for the best classifier based on the training, testing and all (training + testing) data sets