Table 3.
Cross-validation via different approaches on simulated data
| OOA_B | OOA_M | OOA_S | |
|---|---|---|---|
| RF | |||
| OOA_B | 92.85% | 1.84% | 5.31% |
| OOA_M | 2.69% | 84.71% | 12.60% |
| OOA_S | 6.17% | 15.38% | 78.44% |
| DL | |||
| OOA_B | 88.03% | 2.54% | 9.43% |
| OOA_M | 3.43% | 77.78% | 18.79% |
| OOA_S | 5.78% | 15.69% | 78.52% |
| DLS | |||
| OOA_B | 99.09% | 0.00% | 0.91% |
| OOA_M | 0.00% | 99.98% | 0.02% |
| OOA_S | 1.25% | 0.30% | 98.45% |
Confusion matrix for misclassification is reported here via RF (random forest), DL (only neural network), and DLS (neural network and sequential Monte Carlo together) for random samples from the models with ABC.