What is AUC-ROC and why is it important in machine learning?
AUC-ROC stands for Area Under the Receiver Operating Characteristic curve. It's a performance measurement for classification problems.
How do you interpret the AUC value?
The AUC value ranges from 0 to 1. A value of 0.5 indicates no discrimination, while a value of 1 indicates perfect discrimination.
What does the ROC curve represent?
The ROC curve plots the true positive rate against the false positive rate at various threshold settings, showing the trade-off between sensitivity and specificity.
Can you give an example of how to calculate AUC-ROC?
Sure! You can calculate AUC-ROC using libraries like scikit-learn in Python, which provides functions to compute the ROC curve and AUC.
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