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# Table 15 Performance Metrics for classification problem

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

Performance measures | How to express them | Interpretation | When 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 students | To 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 is | To find a balance between recall and precision. |

ROC curve | Plotted 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. |