Table 2. Algorithm performance.

	Precisiona	Recallb	F1-scorec
K	0.86	0.75	0.8
S	0.86	0.89	0.88
T	0.85	0.65	0.74
Z	0.86	0.72	0.78
Macroaverage	0.86	0.75	0.8

^aPrecision is the ability of a classification model to return only the data points in a class. It is calculated by dividing the true positives by the sum of the true positives and false positives.

^bRecall is the ability of a classification model to identify all data points in a relevant class. It is calculated by dividing the true positives by the sum of the true positives and false negatives.

^cF₁-scores are a single metric that combines recall and precision using the harmonic mean. It is calculated by dividing the true positives by the sum of the true positives plus half of the sum of the false positives and false negatives.