90% F1 Score in Relation Triple Extraction: Is it Real? [PDF]
Proceedings of the 1st GenBench Workshop on (Benchmarking) Generalisation in NLP, 2023 Accepted in GenBench workshop @ EMNLP ...
Pratik Saini +3 more
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IEEE AccessThe F1 score has been widely used to measure the performance of machine learning models. However, it is variant to the ratio of the positive class in the training data, $\pi $ .
Hyeon Gyu Kim, Yoohyun Park
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Applied SciencesThis study compares various F1-score variants—micro, macro, and weighted—to assess their performance in evaluating text-based emotion classification. Lexicon distillation is employed using the multilabel emotion-annotated datasets XED and GoEmotions. The
Maria Cristina Hinojosa Lee +2 more
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The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. [PDF]
BMC Genomics, 2020 AbstractBackgroundTo evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet.
Chicco D, Jurman G.
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High-F1-score Recognition of Input Gestures while Holding Smartphone by Reducing False Detection Due to Walking Noise [PDF]
Sensors and Materials, 2022 Ryo Katsuma, Tomoaki Amiya
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Estimating the Uncertainty of Average F1 Scores [PDF]
Proceedings of the 2015 International Conference on The Theory of Information Retrieval, 2015 In multi-class text classification, the performance (effectiveness) of a classifier is usually measured by micro-averaged and macro-averaged F1 scores. However, the scores themselves do not tell us how reliable they are in terms of forecasting the classifier's future performance on unseen data.
Zhang, Dell, Wang, J., Zhao, X.
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X-ray is not inferior to CT in terms of F1 score in the diagnosis of foreign body aspiration: a recall, precision and F1 score performance analysis based on bronchoscopically proven cases. [PDF]
J Pediatr (Rio J)Sarac F, Yazici M.
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A Bayesian Hierarchical Model for Comparing Average F1 Scores [PDF]
2015 IEEE International Conference on Data Mining, 2015 In multi-class text classification, the performance (effectiveness) of a classifier is usually measured by micro-averaged and macro-averaged F1 scores. However, the scores themselves do not tell us how reliable they are in terms of forecasting the classifier's future performance on unseen data.
Zhang, Dell +3 more
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Time to retire F1-binary score for action unit detection
Pattern Recognition Letters, 2023 Detecting action units is an important task in face analysis, especially in facial expression recognition. This is due, in part, to the idea that expressions can be decomposed into multiple action units. To evaluate systems that detect action units, F1-binary score is often used as the evaluation metric.
Saurabh Hinduja +3 more
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Confidence interval for micro-averaged F1 and macro-averaged F1 scores [PDF]
Applied Intelligence, 2021 AbstractA binary classification problem is common in medical field, and we often use sensitivity, specificity, accuracy, negative and positive predictive values as measures of performance of a binary predictor. In computer science, a classifier is usually evaluated with precision (positive predictive value) and recall (sensitivity). As a single summary
Kanae Takahashi +3 more
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