Classification Metrics
Confusion matrix
The confusion matrix is used to have a more complete picture when assessing the performance of a model and deal with skewed data. It is defined as follows:
Main metrics
| Metric | Formula | Interpretaion |
|---|---|---|
| Accuracy | $\frac {TP + TN}{TP + TN + FP + FN}$ | Overall performance of model |
| Precision | $\frac {TP}{TP+FP}$ | How accurate the positive predictions are |
| Recall/ Sensitivity | $\frac{TP}{TP+FN}$ | Converge of actual positive sample |
| Specificity | $\frac{TN}{TN+FP}$ | Converge of actual negative sample |
| F1 score | $\frac{2TP}{2TP+FP+FN}$ | Hybrid metric useful for unbalanced classes |
ROC
The receiver operating curce, also noted ROC, is the plot of TPR versus FPR by varying the threshold. These metrics are summed up below:
| Metric | Formula | Equivalent |
|---|---|---|
| True Positive Rate, TPR | $\frac{TP}{TP+FN}$ | Recall, sensitivity |
| False Positive Rate, FPR | $\frac{FP}{TN+FP}$ | 1-specificity |
AUC
The area under the receiving operating curve, also noted AUC or AUROC, is the area below the ROC as shown in the following figure:
