AUC-ROC
A commonly used metric for evaluating the overall performance of binary classification models, ranging from 0.5 to 1.0. It measures the probability that a randomly chosen positive sample is ranked ahead of a randomly chosen negative sample, is insensitive to class imbalance, and is widely used in medical imaging diagnosis evaluation.
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The classifier trained entirely on synthetic images achieved an AUC-ROC of 0.802 on the real test set, while the baseline trained only on real data achieved 0.760.
In imbalanced classification problems, a model can achieve very high accuracy even by predicting all samples as the majority class, so it is necessary to shift to more discriminative metrics such as precision-recall curves, F1 score, Matthews correlation coefficient, or AUC-ROC
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The classifier trained entirely on synthetic images achieved an AUC-ROC of 0.802 on the real test set, while the baseline trained only on real data achieved 0.760.
50%UnverifiedIn imbalanced classification problems, a model can achieve very high accuracy even by predicting all samples as the majority class, so it is necessary to shift to more discriminative metrics such as precision-recall curves, F1 score, Matthews correlation coefficient, or AUC-ROC
50%UnverifiedAUC的概率意义是随机抽取一个正例和一个负例，模型对正例打分高于负例的概率，该特性使其对类别不平衡不敏感
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