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Can you describe the purpose and interpretation of an ROC curve in model evaluation? How would you contrast bagging with boosting in ensemble learning?
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General
Focus on explaining how ROC curves evaluate classification models through the true positive and false positive rates, then compare how bagging creates parallel models while boosting creates sequential ones.
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