Coordination classification is complicated by the class imbalances inherent to our data set, since 4-fold coordination is, in general, under-represented compared to 5-and 6-fold coordination in TMOs. In training our models, we use random over-sampling (over-sampling the minority class with replacement until parity is reached with the number of the majority class) to ameliorate the effect of class imbalance, and found that this improved model performance on the validation set.