After feature engineering, we wanted to compare different ML models and assess their performance given a predictive task, mapping reaction conditions to yield and make predictions for unseen conditions. Different data structures and featurization methods can deliver varying performance with different ML models, something which cannot be predicted a priori, meaning empirical evaluation is required.