Table 2. Test-set performance of four regression models*.

Target	Model	R 2	MAE	RMSE
Abbreviation: R²=coefficient of determination; MAE=mean absolute error; RMSE=root mean squared error. * All outcomes were log-transformed. † indicates the primary model used in this study.
Cooking oil	Linear regression	0.432	0.878	1.226
	Random forest	0.438	0.989	1.225
	LightGBM multi-task	0.527	0.860	1.124
	LightGBM stage-wise (proposed) †	0.550	0.831	1.069
Salt-containing seasonings	Linear regression	0.527	0.664	0.870
	Random forest	0.393	0.762	0.994
	LightGBM multi-task	0.544	0.656	0.862
	LightGBM stage-wise (proposed)†	0.570	0.632	0.813
Sodium in salt-containing seasonings	Linear regression	0.310	0.662	0.925
	Random forest	0.323	0.727	0.931
	LightGBM multi-task	0.426	0.655	0.858
	LightGBM stage-wise (proposed)†	0.454	0.624	0.806
Salt	Linear regression	0.142	0.557	0.711
	Random forest	0.175	0.547	0.704
	LightGBM multi-task	0.165	0.539	0.708
	LightGBM stage-wise (proposed)†	0.224	0.518	0.666
Total sodium in seasonings and salt	Linear regression	0.303	0.528	0.746
	Random forest	0.288	0.553	0.790
	LightGBM multi-task	0.327	0.545	0.768
	LightGBM stage-wise (proposed)†	0.394	0.506	0.707