The performance of all models parametrized on the LEF4000 is reported in Table . A large part of the problem of predicting 19 F NMR chemical shifts appeared to be linear as demonstrated by the high performance of Lasso regression. This is in line with older methods that attempted to model chemical shifts as linear combinations of substructure contributions with impressive success. KNN tended to degenerate into a single nearest neighbor search by cosine similarity. When trained on all data, the best performing KNN models only considered one neighbor (see Table of the supporting information). The top-performing models were RF, GBT, and Chemprop, which performed quite similarly. The most difference between the three models was observed for the CF2 motif, which had the least training data. The higher standard deviations across the 5-fold cross-validation for CF and CF3, as well as the erratic learning curve (Figure of the supporting information) shed doubt on the slightly better performance of Chemprop in CF and CF3. This doubt was substantiated when all parameterized models were used to predict molecules with CF motifs from tranche 2 after being trained on the full LEF4000. Table shows the top-performing models outperforming lasso regression and KNN more clearly, with the two tree-based models taking the lead in mean absolute error (MAE). Chemprop manages to outperform RF in rootmean-square error (RMSE) but is outperformed by GBT in all metrics. The GBT model was determined to be the most effective method at this point.