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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does ROC stand for?
💡 Hint: Think of the type of curve that evaluates classification decisions.
Question 2
Easy
What does a high AUC value indicate?
💡 Hint: Consider what the value represents on a scale.
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 is the main purpose of the ROC curve?
- To compare multiple models
- To visualize classifier performance
- To calculate accuracy
💡 Hint: Think about the information that the ROC curve represents.
Question 2
True or False: AUC values can only range from 0 to 1.
- True
- False
💡 Hint: Think of AUC as a measure of model reliability.
Solve and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Given a dataset with a high imbalance (e.g., 90% negative and 10% positive), would you prioritize recall or precision when tuning your model? Justify your reasoning.
💡 Hint: Consider the consequences of false negatives in your context.
Question 2
How would you approach modifying a model that shows low precision but very high recall in an imbalanced dataset? What steps would you take?
💡 Hint: Think about how modifying thresholds and model parameters can enhance results.
Challenge and get performance evaluation