MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -The true positive rate (TPR, also called sensitivity) is calculated as TP/TP+FN. TPR is the probability that an actual positive will test positive. The true negative rate (also called specificity), which is the probability that an actual negative will test negative.

Detailed explanation-2: -Definition. In machine learning, the true positive rate, also referred to sensitivity or recall, is used to measure the percentage of actual positives which are correctly identified.

Detailed explanation-3: -The true positive rate (TPR) gives the proportion of correct predictions in predictions of positive class.

Detailed explanation-4: -TPR (True Positive Rate) = # True positives / # positives = Recall = TP / (TP+FN) FPR (False Positive Rate) = # False Positives / # negatives = FP / (FP+TN)

Detailed explanation-5: -TPR refers to the ratio of correctly classified positives to the total number of positive instances in the data. TNR refers to the ratio of correctly classified negatives to the total number of negative instances in the data.