Results 31 to 40 of about 201,162 (313)
Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models [PDF]
Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems, 2020 In pursuit of the perfect supervised NLP classifier, razor thin margins and low-resource test sets can make modeling decisions difficult. Popular metrics such as Accuracy, Precision, and Recall are often insufficient as they fail to give a complete picture of the model’s behavior. We present a probabilistic extension of Precision, Recall, and F1 score,
Reda Yacouby, Dustin Axman
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F1-score of prediction models by incorporating information from the last 7 days using the test set.
, 2023 F1-score of prediction models by incorporating information from the last 7 days using the test set.
Shazia Usmani (8684598) +1 more
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F1-score of prediction models by incorporating information from the last 10 days using the test set.
, 2023 F1-score of prediction models by incorporating information from the last 10 days using the test set.
Shazia Usmani (8684598) +1 more
core +1 more source
Estimating the $$F_1$$ Score for Learning from Positive and Unlabeled Examples [PDF]
, 2020 Semi-supervised learning can be applied to datasets that contain both labeled and unlabeled instances and can result in more accurate predictions compared to fully supervised or unsupervised learning in case limited labeled data is available. A subclass of problems, called Positive-Unlabeled (PU) learning, focuses on cases in which the labeled ...
S.A. Tabatabaei (Seyed Amin) +2 more
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F1-score fluctuations of the MLP model per epoch.
, 2023 Background and objectiveNon-suicidal self-injury (NSSI) is a psychological disorder that the sufferer consciously damages their body tissues, often too severe that requires intensive care medicine.
Nacer Farajzadeh (15338964) +1 more
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Remote Sensing, 2021 Infrared small-object segmentation (ISOS) has a persistent trade-off problem—that is, which came first, recall or precision? Constructing a fine balance between of them is, au fond, of vital importance to obtain the best performance in real applications,
Ikhwan Song, Sungho Kim
doaj +1 more source
F1 score and numbers of queries when each model reached the maximum F1 score.
, 2022 F1 score and numbers of queries when each model reached the maximum F1 score.
Mengmeng Zhang (1507543) +2 more
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Automatic Feature Segmentation in Dental Periapical Radiographs
Diagnostics, 2022 While a large number of archived digital images make it easy for radiology to provide data for Artificial Intelligence (AI) evaluation; AI algorithms are more and more applied in detecting diseases.
Tugba Ari +10 more
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Relationship between F1 score and proofreading time.
, 2023 (A) A diagram explaining the F1 score. (B) Times required for proofreading 100 × 512 × 512 voxel mitochondrial predictions with various F1 scores until the scores exceed 0.96 were measured. Note that the y-axis is presented in log scale.
Koki Nakamura (5340158) +5 more
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F1 Score about different categories.
, 2022 F1 Score about different categories.
Liming Wang (114648) +3 more
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