Table 1.

Number of features, R${}^2$ score, Pearson correlation (R), Major Error (ME), Very Major Error (VME), area under the receiver operating curve (AUC), Accuracy within a two/four-fold dilution (ACC-2, ACC-4) and Mean Absolute Fold Error (MAFE) on the unseen test data. For the AUC, ME, VME the data was binarized using 1 mg/L threshold.

Model	N_feat	R${}^2$${}^{\text{b}}$	R${}^{\text{b}}$	ME${}^{\text{a,c}}$	VME${}^{\text{a,c}}$	AUC	ACC-2	ACC-4	MAFE${}^{\text{c}}$
Random forest	4	0.932	0.966	1	0	1.000	0.658	0.944	0.883
Random forest	15	0.902	0.951	5	0	0.996	0.680	0.914	0.915
Linear regression	4	0.918	0.959	0	2	1.000	0.650	0.929	0.954

The number of features were selected according to the performance using leave-one-country-out validation on the training data, see Supplementary Fig. S1.

${}^{\text{a}}$Number of samples.

${}^{\text{b}}$Calculated on the log2 values.

${}^{\text{c}}$The lower the better.