## 7 Experiments and Results
The sixth classifier is the SCN in Figure 3. We trained this architecture on the training and testing data of each dataset and persisted all trained models. A grid search of the parameters such as learning rate, weight decay, and dropout percentage was performed on each dataset. In the grid search, the learning rate \(\alpha\) was confined to the values 0.001, 0.01, and 0.1; the L2 weight decay was confined to 0.0001, 0.001, 0.01, and 0; the dropout was confined to 0, 0.2, 0.3, 0.4, and 0.5. The model training used the model checkpoint to save the best model based on the lowest testing loss in all epochs. All SCNs were trained on the color images of all datasets. The validation accuracies for the overall best models for each dataset are given in Table 3. While the SCNs performed better than the RRFs on four datasets BEE1, BEE2_1S, BEE3_NS, and BEE3_S, the RRFs outperformed the SCNs on the more challenging datasets BEE2_2S and BEE4. These datasets are more challenging, because the camera is positioned above the second super of the Langstroth beehive. Thus, the average number of pixels per honey bee is smaller. The HQL and DQL 2 RRFs performed on par on all datasets, except for BEE2_2S, where DQL 2 slightly outperformed HQL. The performance of the DQL 1 RRFs was the worst on all datasets. On CIFAR-10, SCNs outperformed all RFs: the performance of the SCN exceeded the performance of SK_RFC by 18.45% and the performance of the HQL RRF by 23.95%. It is instructive to inspect the validation accuracy curves of the HQL RRFs and PRFs on various datasets with the values of \(\alpha\) and \(\gamma\) parameters (See Equation (1)) used in the grid search. Figure 7 indicates that on BEE1 all RFs kept improving performance until the number of the DTs in them reached 30, after which their validation accuracy varied between 90% and 95%. The PRF's accuracy reached its peak performance of 91% when the number of DTs in it was 20, after which it started slowly declining until its number DTs exceeded 75, after which the rate of decline accelerated. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **HQL** & **DQL1** & **DQL2** & **PRF** & **SKL\_RF** & **SKL\_RFC** & **SCN** \\ \hline BEE1 & 91.92 & 84.89 & 89.88 & 91.35 & 93.31 & 93.79 & 97.00 \\ BEE2\_1S & 78.12 & 68.93 & 79.68 & 80.10 & 76.09 & 63.00 & 89.16 \\ BEE2\_2S & 78.92 & 71.63 & 79.05 & 78.78 & 77.76 & 74.46 & 76.29 \\ BEE4 & 81.71 & 73.72 & 81.10 & 80.95 & 79.68 & 74.61 & 77.85 \\ BEE3\_NS & 87.07 & 77.77 & 87.22 & 86.91 & 87.54 & 81.74 & 90.52 \\ BEE3\_S & 84.08 & 70.97 & 83.35 & 83.77 & 83.83 & 83.35 & 88.08 \\ CIFAR-10 & 41.62 & 25.45 & 40.56 & 43.11 & 41.90 & 47.07 & 65.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Validation accuracies (in %) of classifiers on all datasets; SKL_RF is scikit-learn RandomForestClassifier trained on grayscale images; SKL_RFC is the scikit-learn RandomForestClassifier trained on color images; BEE3_NS is BEE3 dataset with SHADOW_BEE images removed; BEE3_S is BEE3 dataset with SHADOW_BEE images; green cells indicate best accuracies. Figure 8 indicates that on BEE2_1S, as the number of DTs in the HQL RFs was increasing, their accuracy was varying between 68% and 70%. The accuracy of the PRF was highest when the number of DTs in it was 5 and then kept declining. The BEE2_1S validation curves suggest that for this dataset, the RFs with smaller numbers of DTs performed better in that their validation accuracy was above 70% and, in the case of the PRF, reached 80%. On BEE2_2S (see Figure 9), one of the two most challenging datasets, all RFs kept improving their performance until the number of DTs in them reached 100, after which their accuracy stabilized around 78%. The PRF's accuracy did not decline with the addition of new DTs. Figure 8: Validation accuracy (in %) vs. number of DTs of HQL RFRs and PRF on BEE2_1S. Figure 7: Validation accuracy (in %) vs. number of DTs of HQL RFRs and PRF on BEE1.