In this section, we train and evaluate machine learning classifiers on experimental data collected from Reaxys consisting of 58,384, 14,739, and 27,465 unique reaction sites in the training, test, and out-of-sample sets, respectively. The experimental data often contain just single or a few reported reactive sites among all reaction sites in the reactant, i.e. there are significantly more negatives (N) than positives (P) in the dataset. Consequently the accuracy (the proportion of correct predictions, ACC = TP+TN P+N ) can be a misleading metric. For example, a "Null model", where all sites are predicted to be non-reactive achieves a respectable accuracy of 76 % (Table ) for both the test and out-of-sample sets, but this just reflects the fact that 76% of the sites in both the datasets are unreactive. The Matthews correlation coefficient 37 (MCC) is a more robust metric to assess the model performance, since it also considers false positives (FP) and false negatives (FN) in addition to true positives (TP), true negatives (TN).