Data Science Stack Exchange is a question and answer site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field. It only takes a minute to sign up.
Sign up to join this community
A confusion matrix is a table that is often used to describe the performance of a classification model (or "classifier") on a set of test data for which the true values are known. The confusion matrix itself is relatively simple to understand, but the related terminology can be confusing.
What is sensitivity in confusion matrix?
Sensitivity or true recall rate:
Sensitivity is calculated as the number of correct positive predictions (TP) divided by the total number of positives (P)
sensitivity = TP/(TP+FN)
It defines the correctness of predictions made. Thus its significance is useful in medical applications where high sensitivity model will give a more relemphasized textiable result in tests of disease.
Yes, the concept is simple but too many terms become difficult to keep in mind. You may save this -
Precision - % of correct positive out of Classifier's positive (TP/(TP+FP) ;
Recall (Sensitivity, True +ve Rate) - % of correct positive out of total positive (TP/(TP+FN) ;
Specificity(True –Ve Rate) = (TN/(TN+FP)) ;
False +Ve Rate =( FP/(FP+TN) )
Sensitivity aka Recall is the number of correctly identified points in the class (true positives; TP) divided by the total number of points in the class (Positives; P).
In your prediction of the class (P), you will correctly identify some points in the class (true positives; TP), and misclassify some points into another class (false negatives; FN). Thus, P = TP + FN
sensitivity = TP/P = TP/(TP+FN)
