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?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — a is correct because Recall = TP / (TP + FN) = 45 / (45 + 15) = 45/60 = 0.75. B (0.90) equals precision: TP/(TP+FP) = 45/50 = 0.90.
Full explanation below image
Full Explanation
A is correct because Recall = TP / (TP + FN) = 45 / (45 + 15) = 45/60 = 0.75. B (0.90) equals precision: TP/(TP+FP) = 45/50 = 0.90. C (0.97) approximates specificity: TN/(TN+FP) = 135/140 ≈ 0.964. D (0.81) is the overall accuracy: (TP+TN)/(TP+FP+FN+TN) = 180/200 = 0.90 — none of these other computations match recall.