## 3 Results
The confusion matrices for the neural network and decision tree are shown in Figures 7 and 8, respectively. The decision tree took about 5 h to train, which is 2.5 times longer than the neural network, most likely due to longer output vectors requiring more training time to generate more divisions in the tree. The decrease in the decision tree performance might be accounted for by the increased complexity of the tree, as more output classifications, 30 classifications instead of 10 classifications, are required. Figure 6: A chart comparing the percentage accuracy and standard deviation of neural networks and decision trees trained on varying subsets of the testing and training datasets, ranging from 100% to 10% of the total data. Figure 7: The confusion matrix corresponding to classification performed using a neural network trained on a dataset with 30-component output vectors. The data correspond to ten transmission eigenchannels of three devices with three different scattering strengths \(N_{\text{s}}\). This classification allows for identification of both the eigenchannel as well as the disorder strength. The ports corresponding to the respective three devices are color coded. The maximum value in any cell can be 6000. We also tested the decision tree on the second multilabel dataset with 13-component output vectors where it achieved an accuracy of 99.44%. This decision tree took approximately 1.5 h to train. The higher accuracy and shorter training time mostly likely correspond to the smaller output vectors. Multilabel confusion matrices require a class-wise comparison with one-versus-rest binarization. This means that for three devices with ten vectors each it produces 30 two-by-two confusion matrices corresponding to the number of true negatives (not classifying an erroneous port), true positives (classifying the correct port), false negatives (not classifying a correct port), and false positives (classifying an erroneous port) for each port of each device. We chose not to include all of these confusion matrices. Finally, to assess the importance of including the transmitted speckle-like intensity distribution in the classification process, we utilized scikit-learn's permutation importance function to identify which features, or values of the intensity distribution, were most vital to the decision tree's accurate performance. The permutation feature importance is defined as the decrease in the model's accuracy when a single feature value is randomly shuffled and indicates how much the model depends on that feature. We evaluated the permutation feature importance on three separate runs with the same configuration. Of the top 20 most important features out of 451, 12 of the 20 were the points near the end of the intensity distribution, between positions 409 and 450, corresponding to \(L>23\) um. These points correspond to the transmitted intensity pattern in the buffer region at the end. When we calculated the mean value and standard deviation of each intensity distribution point separately for each port, we noticed that the points near the end of intensity distribution, between positions 412 and 450, consistently had the lowest standard deviation, even while their values differed significantly between ports. The features with the smallest standard deviation between samples of the same port were most important in the classification process and in distinguishing between ports. This gives some insight into the decision tree's accurate performance.