## 3 Experiments and Results
### Classification and Regression Metrics  
In this paper, we are dealing with a classification and regression problem, so it is necessary to analyze the metrics required in each case. We will begin with a description of the classification metrics used in this study, whose scalar and graphical analysis allows us to perform a correct interpretation for the evaluation of the different models [47]. We consider four scalar metrics: the area under the ROC curve (AUC), precision, recall and F-score (sometimes known as F1-score). In some cases, we will add the ROC curve as a graphical interpretation of the results. A brief description of these metrics is as follows:  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Hyper-Parameter** & **Meaning** & **Values** \\ \hline \multirow{3}{*}{DT} & criterion & Function to measure the quality of a split & squared error \\  & maxDepth & Maximum allowed depth for trees & unbounded \\  & min\_samples\_split & Number of samples required to split an & 2 \\  & & internal node & \\ \hline \multirow{3}{*}{RF} & \(N\) & Number of weak learners (trees) & 100 \\  & maxDepth & Maximum allowed depth for trees & unbounded \\ \hline \multirow{3}{*}{SVR} & \(K\) & Kernel & sigmoid \\  & \(\gamma\) & Kernel coefficient & \(\frac{1}{(\kappa\_sample\_unitless\kappa)}\) \\ \cline{1-1}  & \(C\) & Regularization parameter & 1 \\ \cline{1-1}  & \(\epsilon\) & No penalty associated with points & 0.1 \\ \hline \multirow{3}{*}{MLP} & \(N\)  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{6}\]  
\[\text{F-score}=2\frac{\text{Precision}\cdot\text{Recall}}{\text{Precision}+ \text{Recall}}, \tag{7}\]  
where \(TP\) is the number of true-positive samples, \(FP\) is the number of false-positive samples and \(FN\) is the number of false-negative samples. Therefore, the precision metric indicates the number of positive samples that were correctly classified with respect to the total number of predicted positive samples (true and false). The recall metric expresses the ratio of correctly classified positive samples with respect to the false-negative and true-positive samples. We must pay special attention to this metric, since a false negative (in our case, suggesting that there was no locust presence when in fact there was) may imply greater losses than false positives. Finally, the F-score metric is the harmonic mean of precision and recall. Analyzing the conclusions derived from the precision and recall metrics, a low F-score value caused by a low recall may imply a worse classifier than if this low value were caused by a low precision. The AUC is related to the ROC curve, so we will first define this graphical classification metric. The ROC curve is a two-dimensional graph where the parameter true-positive rate (TPR), or recall, represents the y-axis, and false-positive rate (\(FPR=FP/FP+TN\), where \(TN\) is the number of true-negative samples) represents the x-axis. So, this helps us to understand the benefits, which would be given by the true positives, versus the costs, given by the false positives, of a binary classifier. The AUC is used to calculate the area under the ROC curve. The values of this parameter range from 0 to 1, so there are no classifiers with good performance with AUC values below 0.5.