Using the feature importance metrics provided by XGBoost , specifically the "gain" metric, we could measure the average accuracy gain of splits in the decision trees using a particular feature. The feature with the highest gain importance resembled a substructure of a fluorine bound to an aromatic substructure. The difference between the average chemical shift of all aliphatic CF groups to all aromatic CF groups was around 60 -70 ppm, demonstrating that the model had extracted a very impactful feature. Typically, fluorines bound to an sp 3 hybridized carbon generate signals in the more shielded (i.e., the more negative) region of the 19 F NMR spectrum than those bound to sp 2 hybridized carbon.