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Posture transitions were detected with a F1 score of 0.79.
Limiting the analysis to just the genetically confirmed cases has an F1 score of 0.82.
On UPMC dataset, the IWE model's average F1 score was 0.94, when the PeFinder scored 0.92 and word2vec scored 0.85.
The best of our approaches produced an average adjective classification F1 score of 0.77, a score higher than that of an average human subject.
It achieves an unweighted average F1 score of 0.895, calculated from multiple evaluation metrics (MUC, B3 and CEAF scores).
Sitting was shown to present a greater challenge to classification with a F1 score of 0.54, compared to the lateral lying postures, which were classified with an average F1 score of 0.91.
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Disambiguation without clustering is much worse (0.830 F1 score).
We ranked third place with a mean F1-score of 63.32%, surpassing the F1-score of the method with highest accuracy by 1.69%.
Accuracy of these methods was measured using an F1-score (harmonic mean of precision and recall).
SVMperf: Learns a binary classification rule that directly optimizes ROC-Area, F1-Score, or the Precision/Recall Break-Even Point.
We tested 22 different combinations of these features and reported precision, recall, F1-score, AUC, and PRC (Fig. 4a).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com