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The Area Under Curve (AUC) is a performance measurement for classification models at various threshold settings, representing the degree or measure of separability between classes. A higher AUC indicates a better model performance, with an AUC of 1.0 representing a perfect model and 0.5 indicating a model with no discriminative power.
A precision-recall curve is a graphical representation used to evaluate the performance of a binary classifier, showing the trade-off between precision (the accuracy of positive predictions) and recall (the ability to find all positive instances) across different thresholds. It is particularly useful in scenarios with imbalanced datasets, where the positive class is rare, as it focuses on the performance of the positive class rather than the overall accuracy.
Test accuracy measures the proportion of correct predictions made by a model on a test dataset, serving as a crucial indicator of its performance and reliability. It is essential for evaluating how well a model generalizes to unseen data, beyond the training set.
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