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Classifier Evaluation Metrics: Precision and Recall, and F-measures
- Precision: exactness – what % of tuples that the classifier labeled as positive are actually positive
\[precision=\frac{TP}{TP+FP}\]
- Recall: completeness – what % of positive tuples did the classifier label as positive?
\[recall=\frac{TP}{TP+FN}\]
- Perfect score is 1.0
- Inverse relationship between precision & recall
- F measure (F1 or F-score): harmonic mean of precision and recall,
\[F=\frac{2\times precision \times recall}{precision+recall}\]
- Fß: weighted measure of precision and recall
- assigns ß times as much weight to recall as to precision
\[F_{\beta }=\frac{(1+\beta ^{2})\times precision \times recall}{\beta ^{2}\times precision+recall }\]
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