8.7 - ROC Curve and AUC
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Practice Questions
Test your understanding with targeted questions
What does ROC stand for?
💡 Hint: Think about a characteristic that helps in evaluating models.
What is the purpose of drawing a ROC curve?
💡 Hint: Focus on visualization related to model evaluation.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What does a higher AUC indicate?
💡 Hint: Remember what an AUC close to 1 means.
True or False: The ROC Curve can be used to compare multiple classification models.
💡 Hint: Think about what the ROC Curve illustrates in terms of models.
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Challenge Problems
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You have trained two classifiers with the following AUC values: Classifier A = 0.85, Classifier B = 0.60. What conclusions can you draw about their performances?
💡 Hint: Recall that higher AUC means better classification ability.
Create a scenario where a model with low accuracy (e.g., 55%) could still have a high AUC value. Explain why AUC can sometimes be misleading.
💡 Hint: Think about imbalanced datasets and the trade-off between precision and recall.
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