## 3 Experiments and Results
### Results and Discussion  
We begin by analyzing the results obtained by the classification machines. The ones considered in this study are SVM, DT and RF, some of the most successful existing classification approaches. Table 3 shows the results obtained for each of these machines, for the three scenarios considered, without applying the SMOTE balancing technique. As can be seen, the best results have been obtained by the RF algorithm in all scenarios, among all ML techniques used. We should highlight those obtained for Scenario 1, with an F-score value of 0.93. In this case, a value of 0.95 has been obtained for the recall metric, and a value of 0.91 for precision. This indicates that we are dealing with a good classifier, with a low false-negative rate, which in this scenario has a higher false-positive rate which causes a lower precision. This seems to be the reason for the low F-score, and not the recall. This classifier therefore allows us to better detect the presence of locusts, with fewer errors. Proof of this is also that the best AUC value obtained, in this case 0.95, is very close to 1, which gives us an idea about the high rate of true positives and the low rate of false positives provided by this classifier. We can also highlight the importance of the use of the meteorological variables chosen for the forecasting process when using RF, noting the influence they can have on the appearance of the Mauritanian locust. As for Scenarios 2 and 3, we see that the worst results are obtained for Scenario 3, with an F-score of 0.75 and a precision and recall of 0.76 and 0.75, respectively. This may be due to the fact that this is the scenario with the lowest number of days of sightings, with a total of 103, compared to Scenario 1 and 2 with 123 and 210, respectively. This implies that the data distribution and how it correlates with the predictor variables are of utmost importance since, even though it has the lowest number of days of sightings, it does not have the lowest number of samples. In all scenarios, SVM and DT obtain the worst results, which may indicate that the meteorological variables used, as well as the number of samples, are not sufficient for these methods to obtain good results in the classification process. Table 4 shows the results obtained by all the classification algorithms used when the SMOTE technique is applied in the pre-processing of the data. Again, the best results are obtained by the RF algorithm for all scenarios. It can be seen that for both Scenarios 1 and 2, the use of the SMOTE technique does not result in a significant improvement, but rather the opposite, going from an F-score value of 0.93 to 0.90 in Scenario 1, and from 0.82 to 0.81 in Scenario 2, in both cases using the RF algorithm. This is not a significant worsening, but this pre-processing method does not contribute to improving the performance of the RF machine. However, the situation in Scenario 3 is different: the SMOTE technique contributes to improving the results of AUC, precision, recall and F-score with values that go from 0.75, 0.76, 0.75 and 0.75, respectively, without the use of SMOTE, to 0.85, 0.77, 0.85 and 0.81 for AUC, precision, recall and F-score, respectively, when the SMOTE technique is applied. This may be due to the fact that, being the scenario with the smallest number of days of sightings, but not the scenario with the smallest number of samples, the use of a technique that involves the creation of synthetic samples favors the best performance in the learning machines. It may be because the dataset allows for better generalization and for the underlying data structure to be captured. This facilitates SMOTE in generating synthetic samples that closely resemble real instances of the minority class, thereby enhancing the model's ability to accurately recognize and classify minority instances. For the rest of the classification algorithms, SVM and DT, an improvement in performance is