See the following Figure and compute the true positive rate

See the following Figure and compute the true positive rate. TPR/, false positive rate. FPR/, Precision and Accuracy. There are two possible predicted classes: \"yes\" and \"no\". If we predict the presence of a disease., for example, \"yes\" would mean they have the disease, and \"no\" would mean they don\'t have the disease. The classifier made a total of 165 predictions (e.g., 165 patients were being tested for the presence of that disease). Out of those 165 cases, the classifier predicted \"yes\" 110 times, and \"no\" 55 times. In reality, 105 patients in the sample have the disease, and 60 patients do not.

Solution

Some of the related terms are as follows:

true positives (TP)
false positives (FP)
false negatives (FN)
true negatives (TN)
Accuracy = (TP+TN)/total
=(100+50)/165 = 150/165 = 0.91

Precision = TP/predicted yes
100/110 = 0.91

True Positive Rate= TP/actual yes
= 100/105 = 0.95

False Positive Rate=FP/actual no
= 10/60 = 0.17