In this work, we use ML as a tool to provide statistical insight on the properties that cause some carbon atoms in N + -GCCs to bind O2. For the distribution of 258 N + -GCCs explored in this work, over 90% contain at least one carbon that DFT predicts to bind O2, but in turn these active sites comprise only 20% of the carbon atoms in the dataset. There is no single structural feature we found that distinguishes the active sites from the inactive sites, making this a suitable problem for analysis with ML models that can draw inferences from many contributing chemical features.