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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does the ROC curve represent?
💡 Hint: Think about how model performance is represented graphically.
Question 2
Easy
What does Precision indicate in a classifier?
💡 Hint: Recall the formula involving true positives.
Practice 4 more questions and get performance evaluation
Interactive Quizzes
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What does the AUC of 1.0 signify?
- Average performance
- Perfect classifier
- No better than random guessing
💡 Hint: Think about the ideal case for classifiers.
Question 2
True or False: The Precision-Recall curve is more informative than the ROC curve when dealing with large imbalances between classes.
- True
- False
💡 Hint: Recall the focus of both curves.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Consider a highly imbalanced dataset where class A has 95% of the data while class B has only 5%. Your model returns a high accuracy but a low recall for class B. Explain the implications of these results and what you should do next.
💡 Hint: Reflect on the value of balanced performance metrics versus overall accuracy.
Question 2
You are tasked with optimizing a model’s decision threshold based on ROC analysis. Describe the process you would use to select an optimal threshold and the factors you would consider.
💡 Hint: What are the costs associated with misclassifications in your context?
Challenge and get performance evaluation