Table 1.

Performance metrics of machine learning algorithms for predicting cannabinoid compounds

ML algorithm	CBGA	CBDA	THCA	CBG	CBD	D9THC	CBC
Coefficient of correlation (r)
BA	0.09	0.4	0.54	0.62	0.51	0.48	0.55
BB	0.05	0.38	0.56	0.62	0.47	0.47	0.56
BC	0.05	0.39	0.57	0.63	0.48	0.49	0.55
RF	0.04	0.34	0.69	0.73	0.59	0.58	0.64
GBLUP	0.09	0.4	0.57	0.66	0.5	0.49	0.59
EN	0.1	0.21	0.38	0.44	0.36	0.33	0.39
SVM	0.15	0.13	0.13	0.18	0.15	0.13	0.13
Coefficient of determination (R 2)
BA	0.008	0.160	0.292	0.384	0.260	0.230	0.303
BB	0.003	0.144	0.314	0.384	0.221	0.221	0.314
BC	0.003	0.152	0.325	0.397	0.230	0.240	0.303
RF	0.002	0.116	0.476	0.533	0.348	0.336	0.410
GBLUP	0.008	0.160	0.325	0.436	0.250	0.240	0.348
EN	0.010	0.044	0.144	0.194	0.130	0.109	0.152
SVM	0.023	0.017	0.017	0.032	0.023	0.017	0.017
Mean squared error (MSE)
BA	0.021	0.013	0.593	0.003	0.001	0.672	0.002
BB	0.022	0.064	0.635	0.003	0.001	0.623	0.001
BC	0.011	0.076	0.731	0.003	0.001	0.796	0.002
RF	0.019	0.180	0.699	0.004	0.002	0.608	0.002
GBLUP	0.013	0.035	0.538	0.004	0.001	0.638	0.001
EN	0.013	0.065	0.869	0.004	0.002	0.861	0.001
SVM	0.011	0.096	0.666	0.003	0.002	0.582	0.003
Root mean squared error (RMSE)
BA	0.00044	0.00017	0.35165	0.00001	0.00000	0.45158	0.00000
BB	0.00048	0.00410	0.40323	0.00001	0.00000	0.38813	0.00000
BC	0.00012	0.00578	0.53436	0.00001	0.00000	0.63362	0.00000
RF	0.00036	0.03240	0.48860	0.00002	0.00000	0.36966	0.00000
GBLUP	0.00017	0.00123	0.28944	0.00002	0.00000	0.40704	0.00000
EN	0.00017	0.00423	0.75516	0.00002	0.00000	0.74132	0.00000
SVM	0.00012	0.00922	0.44356	0.00001	0.00000	0.33872	0.00001

BA, Bayesian Algorithm; BB, Bayesian Bootstrapping; BC, Bayesian Classification; CBC, Cannabichromene; CBD, Cannabidiol; CBDA, Cannabidiolic Acid; CBG, Cannabigerol; CBGA, Cannabigerolic Acid; D9THC, Delta‐9‐Tetrahydrocannabinol; EN, Elastic Net; GBLUP, Genomic Best Linear Unbiased Prediction; ML Algorithm, Machine Learning Algorithm; RF, Random Forest; SVM, Support Vector Machine; THCA, Tetrahydrocannabinolic Acid.