A predictive-maintenance team evaluates a binary classifier that labels a component as Failure (positive class) or No Failure (negative class). On a hold-out test set of 1,000 components, the confusion matrix is:
Predicted Failure Predicted No Failure
Actual Failure 168 32 Actual No Failure 42 758
What value, rounded to two decimal places, best represents the F1 score of this classifier?
The value 0.84 is the recall, 0.80 is the precision, and 0.93 is close to the overall accuracy ((168 + 758) / 1000). Therefore, 0.82 is the only choice that correctly represents the F1 score.
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How is the confusion matrix used to calculate precision and recall?
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