## 3 Results  
Over ten runs, the best decision tree classifier achieved an average accuracy of 99.43% (standard deviation: 0.0112%), and the best fully-connected neural network had an average accuracy of 98.07% (standard deviation: 0.727%). We note here that these accuracies are comparable to the accuracies obtained using the state-of-the-art classification techniques [36]. Figures 4 and 5 show the confusion matrices for the best neural network and best decision tree, respectively. The port labels are listed along the left side and the bottom. With i referring to the rows and j referring to the columns, the ij-th entry in the confusion matrix is the number of intensity distributions classified as port i whose correct label was port j. The diagonal from top left to bottom right entries have i = j, meaning that those intensity distributions were correctly classified. The neural network's confusion matrix shows that the network had the hardest time distinguishing between ports 0 and 1, ports 1 and 2, and ports 8 and 9 (Figure 4). Alternatively, the decision tree's confusion matrix shows that the decision tree had the hardest time distinguishing between ports 0 and 1, ports 7 and 8, and ports 8 and 9 (Figure 5). We tested the best neural network and best decision tree classifier, trained on the full dataset, against the datasets of the individual optical devices. We did not see a significant change in performance. This is expected since we were testing the trained models on subsets of the original full dataset. We compared a decision tree trained on one-hot vector encodings and a decision tree trained on label encodings of the port number. With label encodings and one-hot vector encodings, the decision tree achieved 99.45% and 99.58% accuracy, respectively. While the branching structures of the decision trees were different, the accuracy was not significantly affected by different encodings. However, the one-hot vector encoding tree took almost twice as long to train as the label encoding tree. Generally, neural networks require much more data than decision tree classifiers to make accurate predictions. In Figure 6, we see that, on average, the decision tree maintained 2-9% higher accuracy than the neural network when trained on iteratively smaller subsets of the full dataset. Also, the accuracies of the trained neural networks had a higher standard deviation than the trained decision tree accuracies (Figure 6). The training of an accurate neural network is partially dependent on the initial, randomly-set weights. Therefore, even with the same hyperparameters, like shape and size of the hidden layers, the accuracy can be very variable (Figure 6). As well as being more accurate, this shows that decision trees also tend to train more dependably. Figure 4: The confusion matrix corresponding to classification performed using a neural network trained on a dataset with 10-component output vectors. The data correspond to ten transmission eigenchannels of three devices with three different scattering strengths \(N_{s}\). This classification allows for identification of the eigenchannel independent of the disorder strength. The maximum value in any cell can be 18,000. Figure 5: The confusion matrix corresponding to classification performed using a decision tree trained on a dataset with 10-component output vectors. The data correspond to ten transmission eigenchannels of three devices with three different scattering strengths \(N_{s}\). This classification allows for identification of the eigenchannel independent of the disorder strength. The maximum value in any cell can be 18,000. Finally, we tested the best neural network and decision tree classifier configurations on the first multilabel dataset with 30-component output vectors. The decision tree and neural network achieved 95.02% and 98.99% accuracy, respectively. Along with correctly identifying the port number, these model configurations allowed us to classify the disorder strength as well.