MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances, while recall (also known as sensitivity ) is the fraction of relevant instances that were retrieved.

Detailed explanation-2: -Recall or Sensitivity is the Ratio of true positives to total (actual) positives in the data. Recall and Sensitivity are one and the same.

Detailed explanation-3: -A higher sensitivity means the rule is more sensitive to the problem (E.g. the issues of a car), but this will likely result in a higher false positive. So overall, recall is among the true positives the percentage successfully identified (recalled).

Detailed explanation-4: -Sensitivity (Recall or True positive rate) Sensitivity (SN) is calculated as the number of correct positive predictions divided by the total number of positives. It is also called recall (REC) or true positive rate (TPR). The best sensitivity is 1.0, whereas the worst is 0.0.

Detailed explanation-5: -Recall, sometimes referred to as ‘sensitivity, is the fraction of retrieved instances among all relevant instances. A perfect classifier has precision and recall both equal to 1. It is often possible to calibrate the number of results returned by a model and improve precision at the expense of recall, or vice versa.