Quantum Machine Learning
The area under the receiver operating characteristic (ROC) curve is a performance metric for binary classification models that quantifies the model's ability to discriminate between positive and negative classes. A higher area indicates better model performance, with a value of 1 representing a perfect classifier and 0.5 indicating no discriminative ability. This concept is crucial in evaluating models such as linear and logistic regression, where decision boundaries can significantly impact classification outcomes.
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