Machine Learning and Applied Statistics Lesson of the Day – Sensitivity and Specificity
May 26, 2014 Leave a comment
To evaluate the predictive accuracy of a binary classifier, two useful (but imperfect) criteria are sensitivity and specificity.
Sensitivity is the proportion of truly positives cases that were classified as positive; thus, it is a measure of how well your classifier identifies positive cases. It is also known as the true positive rate. Formally,
Specificity is the proportion of truly negative cases that were classified as negative; thus, it is a measure of how well your classifier identifies negative cases. It is also known as the true negative rate. Formally,