Table 2 Confusion matrix formulas.
From: An effective brain stroke diagnosis strategy based on feature extraction and hybrid classifier
Measure | Formula | Intuitive meaning |
|---|---|---|
Accuracy (A) | \(\frac{XP + XN}{{XP + XN + YN + XN}}\) | The proportion of accurate predictions |
Error (E) | 1 − Accuracy | The proportion of forecasts that are wrong |
Precision (P) | \(\frac{XP}{{XP + YP}}\) | The proportion of accurate positive predictions |
Recall/Sensitivity (R) | \(\frac{XP}{{XP + YN}}\) | The proportion of cases with positive labels that were anticipated to be positive |
F-measure | \(\frac{2 \cdot P \cdot R}{{P + R}}\) | The precision and recall |
