Skip to main content

Table 2 Evaluation metrics: descriptions and equations

From: Supervised learning for infection risk inference using pathology data

Metric Description Equation
Sensitivity Proportion of observed positives that are correctly identified as such (i.e. percentage of culture-positive profiles correctly identified as positive). Also called recall (REC) or true positive rate (TPR). \(SENS=\frac {TP}{TP+FN}\)
Specificity Proportion of observed negatives that are correctly identified as such (i.e. percentage of culture-negative profiles correctly identified as negative). Also called true negative rate (TNR). \(SPEC=\frac {TN}{TN+FP}\)
ROC This curve illustrates the performance of a binary classifier as its discrimnation threshold is varied by plotting true positive rate (TPR) against false positive rate (FPR). It is related to cost/benefit analysis of diagnostic decision making.  
PR This curve represents precision against recall where high scores for both shows that the classifier is returning accurate results (high precision) as well as returning a majority of all positive results (high recall).