Table 4 The goodness-of-fit statistics of number of trees predictions for the ANNs types and multiple regression model
Technique | SSE | \( {R}_{\mathrm{adj}}^2 \) | MSE | RMSE | AIC | BIC |
|---|---|---|---|---|---|---|
Multiple Linear Regression Model | 15.3269 | 0.5431 | 0.1548 | 0.3935 | 0.2690 | −64.5107 |
ANN based on MLP 4–35–1 | 14.3124 | 0.5733 | 0.1446 | 0.3802 | 0.1320 | −68.3460 |
ANN based on MLP 4–13-1 | 14.1687 | 0.5776 | 0.1431 | 0.3783 | 0.1118 | −68.9111 |
ANN based on MLP 4–43-1 | 14.5520 | 0.5662 | 0.1470 | 0.3834 | 0.1652 | −67.4163 |
ANN based on MLP 4–36-1 | 14.1860 | 0.5771 | 0.1433 | 0.3785 | 0.1143 | −68.8426 |
ANN based on MLP 4–2-1 | 14.4645 | 0.5688 | 0.1461 | 0.3822 | 0.1532 | −67.7539 |
ANN based on RBF 4–11-1 | 17.2275 | 0.4864 | 0.1740 | 0.4172 | 0.5028 | −57.9646 |
ANN based on RBF 4–44-1 | 13.8082 | 0.5884 | 0.1395 | 0.3735 | 0.0603 | −70.3543 |
ANN based on RBF 4–5-1 | 16.3146 | 0.5137 | 0.1648 | 0.4059 | 0.3939 | −61.0135 |
ANN based on RBF 4–8-1 | 14.7636 | 0.5599 | 0.1491 | 0.3862 | 0.1941 | −66.6077 |
ANN based on RBF 4–19-1 | 12.1040 | 0.6392 | 0.1223 | 0.3497 | 0.1044 | −77.7310 |