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Table 15 Performance Metrics for classification problem

From: Predicting academic success in higher education: literature review and best practices

Performance measuresHow to express themInterpretationWhen to use
Accuracy\( \frac{TP+ TN}{TP+ TN+ FP+ FN} \)The number of all correct predictions made by the algorithm over all type of predictions made.If the data is almost balanced.
Recall (Sensitivity/TP rate)\( \frac{TP}{TP+ FN} \)The proportion of successful students that classified correctly as “successful”, for all successful studentsTo concentrate on minimizing FN.
Precision\( \frac{TP}{TP+ FP} \)The proportion of successful students that classified correctly as “successful”, for all students predicted by the algorithm as a “successful” student.To concentrate on minimizing FP.
Specificity (TN rate)\( \frac{FP}{TN+ FP} \)the proportion of non-successful students that are incorrectly considered as successful students, for all non-successful students.To identify negative results.
F-Measure\( \frac{2\times Precision\times Recall}{Precision+ Recall} \)How precise your classifier is, as well as how robust it isTo find a balance between recall and precision.
ROC curvePlotted at TP rate vs. FP rate where the TP rate is on the Y axis and the FP rate is on the X axis.The area under the curve (AUC):
• If near to the 1, means the model has high class separation capacity.
• If near to the 0, means the model has no class separation capacity.
Used as a summary of the model’s skill.