A classification model on a 95% negative / 5% positive dataset predicts 'negative' for every observation. What is the F1 score for the positive class?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — c is correct because a model that always predicts negative has zero true positives for the positive class, making recall = 0 and precision undefined (treated as 0). F1 = 2(00)/(0+0) = 0.
Full explanation below image
Full Explanation
C is correct because a model that always predicts negative has zero true positives for the positive class, making recall = 0 and precision undefined (treated as 0). F1 = 2(00)/(0+0) = 0. A (0.95 accuracy) is the naive accuracy, not F1. B (0.50) has no basis. D (0.05) reflects class proportion, not F1.