Table 3.
The performance of the deep learning prediction model on training and test sets.
| Data set | Total samples | RMSE | log loss | MCE | AUC | Gini | Accuracy | Sensitivity | Specitivity | TPV | TNV |
|---|---|---|---|---|---|---|---|---|---|---|---|
| White rice 2014 (Training set) | 60 | 0.45 | 0.55 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
| White rice 2015 (A) (Test set 1) | 40 | 0.54 | 0.83 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
| White rice 2015 (B) (Test set 2) | 26 | 0.46 | 0.59 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
RMSE: Root mean squared error.
LogLoss: Logarithmic loss.
MCE: Mean per-class error.
AUC: Area under the ROC curve.
Gini: Gini coefficient.
TPR: True positive rate.
TNR: True negative rate.