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committedMay 4, 2016
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‎faq/computing-the-f1-score.md

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@@ -107,6 +107,8 @@ ACC <sub>avg</sub> = (TP + TN) / N
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The two approaches are identical, or with a more concrete example: (30 + 40) / 100 = (30/50 + 40/50) / 2 = 0.7.
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Eventually, Forman and Scholz plaid this game of using different ways to compute the F1 score based on a benchmark dataset with a high-class imbalance (a bit exaggerated for demonstration purposes but not untypical when working with text data). It turns out that the resulting scores (from the identical model) differed substantially:
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- F1<sub>avg</sub>: 69%

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