Mind and body connection in expert meditators: a computational study based on central and peripheral nervous system

Table 3 Leave-one-out cross-validation method: Three learning algorithms were compared, i.e., 1) Logistic Regression, 2) Naïve Bayes, 3) Random Forest. In the analysis, the classification learning algorithms were used for the classifications referring to the EEG

	Model	AUC	CA	F1	Precision	Recall
Theta	Logistic Regression	0,88	0,85	0,85	0,85	0,85
	Naive Bayes	0,97	0,85	0,85	0,85	0,85
	Random Forest	0,98	0,85	0,847	0,88	0,85
Low alpha	Logistic Regression	0,57	0,55	0,54	0,55	0,55
	Naive Bayes	0,75	0,70	0,69	0,70	0,70
	Random Forest	0,71	0,700	0,69	0,70	0,70
High alpha	Logistic Regression	0,60	0,60	0,59	0,60	0,60
	Naive Bayes	0,73	0,75	0,75	0,75	0,75
	Random Forest	0,82	0,80	0,80	0,80	0,80
Beta	Logistic Regression	0,73	0,75	0,74	0,75	0,75
	Naive Bayes	0,89	0,85	0,85	0,85	0,85
	Random Forest	0,89	0,85	0,85	0,80	0,80

AUC (Area under the ROC curve) is the area under the classic receiver-operating curve
CA (Classification accuracy) represents the proportion of the examples that were classified correctly
F1 represents the weighted harmonic average of the precision and recall (defined below)
Precision represents a proportion of true positives among all the instances classified as positive. In our case, the proportion of a condition was identified correctly
Recall represents the proportion of true positives among the positive instances in our data

ISSN: 2662-7671