Table 1.

LGBM model comparison in detecting subjective cannabis intoxication (not-intoxicated vs low-intoxication vs. moderate-intensive (MI) intoxication)

LGBM Model	Accuracy	Precision	Recall	F1-Score	Moderate-Intensive Intox AUC
LGBM-DT	0.60	0.72	0.60	0.64	0.82
LGBM-S	0.67	0.83	0.66	0.73	0.90
LGBM-DTS	0.90	0.92	0.90	0.90	0.98

Three Light Gradient Boosting Machine (LGBM) model performance on the test or “holdout” (20%) dataset. Intox: Intoxication LGBM-DT: Day of the week and time of day combined model, LGBM-S: Smartphone-based sensors only model, and LGBM-DTS: Smartphone-based sensors combined with the two time features (day of the week and time of day) in identifying three classes: not-intoxicated (0), low-intoxication (1-3) and moderate-intensive intoxication (4-10), the cutoff between 3 and 4 is defined based on the median value (= 3.0 out of 10.0) of subjective intoxication episodes.