What does Precision measure in classification problems?
- False Positives / Total predictions
- True Negatives / (True Negatives + False Positives)
- True Positives / (True Positives + False Negatives)
- True Positives / (True Positives + False Positives)
Precision is the ratio of true positive predictions to the sum of true positives and false positives. It focuses on the accuracy of the positive predictions and is particularly important when the cost of false positives is high.
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