The area under the ROC curve (AUC) quantifies the overall ability of a binary classification model to discriminate between positive and negative classes. AUC measures how well the model can distinguish between classes across all classification thresholds, with values ranging from 0 to 1, where 0.5 indicates no discrimination (like random guessing) and 1.0 indicates perfect discrimination. This metric is crucial for evaluating model performance, especially in supervised learning tasks, and is integral to assessing the efficacy of data preprocessing methods that impact model input features.
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