Table 6.

Stratified 10-fold Cross validation for the indices of PIT 360°¹.

Method	AUC	CA	F1	Precision	Recall
Logistic Regression	0.579	0.579	0.556	0.588	0.526
Random Forest	0.500	0.500	0.457	0.500	0.421
Support Vector Machine (SVM)	0.605	0.605	0.516	0.617	0.421
Naïve Bayes	0.658	0.658	0.606	0.714	0.526

¹AUC (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 condition correctly identified; Recall represents the proportion of true positives among the positive instances in our data.