## 3 Results
As a test set member, the percentages of good predictions become 44.44% and 0%, respectively (Table S2). This explains the position of chemical #1618 in the left part of Figure 3 near the y-axis. However, even if it is included in the training set of the ML1 and MH1 models, chemical #1618 is predicted as a non-repellent by them (Table S1). The MlogP versus MHlogP values for that compound equal 2.38 versus 4.15 (Table S2), with a low reliability for both calculated log \(P\) values. It is interesting to note that repellent #996 (2,6-dimethyl-1,2,3,6-tetrahydrobenzaldehyde oxime) shows broadly the same behavior as chemical #1618, because as member of the test sets of the ML and MH models, inspection of Table S2 shows that 80% and 20% of correct assignment to class 1 are obtained, respectively. This explains the location of chemical #996 in the top left corner of Figure 3. When it belongs to the training sets, 95.45% and 86.67% of good predictions are reached, respectively (Table S2). This is in agreement with the correct predictions of the ML1 and MH1 models. Its MlogP value is 1.88 with a low reliability, while its MHlogP equals 3.62 with a moderate reliability (Table S2). In contrast, the non-repellent #2123 (2,4,6-trimethyl-1,2,5,6-tetrahydrobenzaldehyde oxime) appears better allocated to class 0 by the MH models than the ML models. Indeed, 91.67% of good predictions are obtained with the MH models versus 59% with the ML models when the chemical belongs to the training sets. As a test set member, 84.62% and 42.86% of correct attributions to class 0 are obtained with the MH and ML models, respectively (Table S2). Note that the ML1 and MH1 models correctly predict the non-repellent activity of the chemical as a training set member (Table S1). The MlogP value of chemical #2123 equals 2.18 with a low reliability, while its MHlogP is 4.17 with a moderate reliability (Table S2). It is noteworthy that in the dataset, there are other molecules including a C=N-OH functional group which are correctly predicted by all the models. Indeed, the non-repellents #475, #476, and #1051 as well as the repellent #1553 have their activity very well predicted by the ML1, MH1, ML, and MH models (Tables S1 and S2). Otherwise, it is also interesting to stress that the two categories of model also behave differently in the prediction of the activity of chemical #500 (_iso_-butyric acid, 2-ethyl-2-hexenal oxime ester). The ML models correctly predict this compound as a non-repellent in 85% of cases when it belongs to the training sets, while the correct predictions total 54.29% when the chemical is included in the test sets. The results are worse with the MH models because 55% of good allocations to class 0 are obtained in the former situation and 11.43% in the latter (Table S2). Lastly, the MlogP value of chemical #500 equals 3.03 and its MHlogP value is 2.02 (Table S2), both log \(P\) predictions having a low reliability. Recently [25], from the same dataset, we showed that the MlogP values of the repellents were distributed over a narrower range than those of the non-repellents. These latter compounds included the greatest log \(P\) values [25]. Consequently, the percentages of good predictions obtained with the ML and MH models were plotted versus the MlogP and MHlogP values of the chemicals, as shown in Figure 4 and Figure 5, respectively. Both figures clearly reveal that the distribution of MHlogP values is much wider than that of MlogP, and that there are non-repellents that cover the whole range of values while being correctly allocated to class 0 by the models. In contrast, the distributions of the calculated log \(P\) values of the repellents are much less widespread than those of the non-repellents, even if the dispersion of MHlogP (Figure 5) values is greater than that of the MlogP values (Figure 4). Chemical #2169 (9-hendecenoic acid, 1-trichloro-2-methyl-2-propyl ester) is the repellent showing the highest calculated log \(P\) values in Figures 4 and 5. Its MlogP and MHlogP values are equal to 4.67 and 7.3, respectively (Table S2).