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AUC (Area Under the Curve): Difference between revisions
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==Why Is AUC Used?== | ==Why Is AUC Used?== | ||
AUC is used to assess the performance of binary classifiers when their classes have significantly more samples than another. In such cases, [[accuracy]] may not reflect true [[precision]] since a classifier may achieve high accuracy by simply correctly predicting which majority class will pass inspection. | AUC is used to assess the performance of binary classifiers when their classes have significantly more samples than another. In such cases, [[accuracy]] may not reflect true [[precision]] since a classifier may achieve high accuracy by simply correctly predicting which [[majority class]] will pass inspection. | ||
AUC provides a more thorough assessment of a classifier's ability to correctly classify positive and negative classes, regardless of class distribution. It has become widely used in various applications such as credit scoring, medical diagnosis, and fraud detection. | AUC provides a more thorough assessment of a classifier's ability to correctly classify positive and negative classes, regardless of class distribution. It has become widely used in various applications such as credit scoring, medical diagnosis, and fraud detection. | ||