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Table 2 Performance metrics [83]

From: Smartphone-based outlier detection: a complex event processing approach for driving behavior detection

Metric Equation
Accuracy is the percentage of instances (evidence) correctly classified. \( \frac{TP+ TN}{TP+ TN+ FP+ FN} \)
Recall is the percentage of instances that were correctly classified as positive. \( \frac{TP}{TP+ FN} \)
Precision is the percentage of instances classified as positive (evidence) that are actually positive. \( \frac{TP}{TP+ FP} \)
F Measure is the harmonic mean of precision and recall, meaning it combines the precision and recall. \( \frac{2\ x\ precision\ \mathrm{x}\ recall}{precision+ recall} \)
Error Rate is the proportion of instances that are incorrectly classified. \( \frac{FP+ FN}{TP+ FN+ FP+ TN} \)