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AUC (Area Under the Curve)

AUC, or Area Under the Curve, is a metric used to evaluate the performance of a model, particularly in classification tasks. Imagine a graph plotting true positive rates against false positive rates; AUC measures the area under this curve. AUC ranges from 0 to 1, where 1 indicates perfect accuracy and 0.5 suggests no discrimination ability, similar to random guessing. A higher AUC value indicates that the model is better at distinguishing between classes, making it a valuable tool for assessing how well a predictive model works.

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  • Image for AUC (Area Under the Curve)

    AUC, or Area Under the Curve, is a performance measure often used in statistics and machine learning, particularly for binary classification models. It quantifies the model's ability to distinguish between two classes. When plotting a graph of the true positive rate against the false positive rate, the AUC represents the total area under this curve. An AUC of 1 indicates perfect performance, while an AUC of 0.5 suggests the model is no better than random guessing. In essence, the higher the AUC, the better the model is at correctly identifying positive and negative cases.