Table 4. Experiments to assess of prediction accuracy of episodix using linear regression, random forest and support vector machines.

	Linear regression				Random forest				Support vector machine
	Precision	F1	Sensitivity (Recall)	Specificity	Precision	F1	Sensitivity (Recall)	Specificity	Precision	F1	Sensitivity (Recall)	Specificity
Episodix [80%–20%] executions = 5	0.73	0.70	0.68	0.83	0.75	0.50	0.38	0.92	0.29	0.25	0.22	0.55
Episodix [80%–20%] executions = 100	0.42	0.40	0.39	0.57	0.74	0.69	0.65	0.82	0.40	0.41	0.41	0.51
Episodix [100%]	0.58	0.56	0.54	0.44	0.69	0.76	0.85	0.44	0.55	0.50	0.46	0.44
Episodix [best 50%]	0.58	0.56	0.54	0.44	0.69	0.76	0.85	0.44	0.57	0.59	0.62	0.33
Episodix (best %) LR (best %) = 5%–6% RF (best %) = 12.5%	0.92	0.88	0.85	0.89	0.86	0.89	0.92	0.78	0.79	0.81	0.85	0.67
Episodix +CVG [100%]	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	0.90	0.83	0.77	0.90
Episodix +CVG [best 50%]	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	0.85	0.83	0.81	0.83
Episodix +CVG (best %) LR (best %) = 50% RF (best %) = 10%	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	0.90	0.83	0.77	0.90

Notes.

Higher quality, when accuracy values were closer to the unit.

CVG

cognitive video games during break of Episodix (similar to the break of CVLT)