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Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does ROC stand for?
π‘ Hint: Think about a characteristic that helps in evaluating models.
Question 2
Easy
What is the purpose of drawing a ROC curve?
π‘ Hint: Focus on visualization related to model evaluation.
Practice 4 more questions and get performance evaluation
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What does a higher AUC indicate?
π‘ Hint: Remember what an AUC close to 1 means.
Question 2
True or False: The ROC Curve can be used to compare multiple classification models.
π‘ Hint: Think about what the ROC Curve illustrates in terms of models.
Solve 2 more questions and get performance evaluation
Push your limits with challenges.
Question 1
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?
π‘ Hint: Recall that higher AUC means better classification ability.
Question 2
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.
π‘ Hint: Think about imbalanced datasets and the trade-off between precision and recall.
Challenge and get performance evaluation