A data scientist constructs a confusion matrix and finds the following values: TP=45, FP=5, FN=15, TN=135. What is the recall (sensitivity) of the model?
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Short Explanation
Here's the deal — recall is TP divided by all actual positives, so you get 45 / (45 + 15) = 0.75. That's the only value that matches sensitivity.
Full Explanation
Recall = TP/(TP+FN) = 45/(45+15)=45/60=0.75, so the 0.75 value is correct. The 0.90 value equals precision, TP/(TP+FP)=45/50=0.90. The 0.97 value approximates specificity, TN/(TN+FP)=135/140≈0.964. The 0.81 value is called overall accuracy, but (TP+TN)/(TP+FP+FN+TN)=180/200=0.90, so none of these other computations match recall.