Recall confusion matrix of the top level generated using 20 randomly generated training and testing sets. After predicting the 20 testing sets, a median, and the 5 and 95 percentiles are provided for each class. This matrix is normalized by dividing each row by the total number of objects per class with known labels. We round these percentages to whole numbers. This indicates a high level of accuracy with a low percentage of incorrectly classified sources.