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Confusion Matrices and Classification Metrics
Confusion Matrices and Classification Metrics
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In binary classification, precision and recall are computed from the confusion matrix. Which formulas are correct?
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A.
Precision = (TP + TN) / (TP + TN + FP + FN); Recall = 1 - precision. Precision is accuracy; recall is its complement.
B.
Precision = TN / (TN + FP); Recall = TN / (TN + FN). Both metrics focus on the negative class by symmetry.
C.
Precision = TP / (TP + FP); Recall = TP / (TP + FN). Precision is over predicted positives; recall is over actual positives.
D.
Precision = TP / (TP + FN); Recall = TP / (TP + FP). Precision is over actual positives; recall is over predicted positives.
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