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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 what happens when you change the decision threshold.
Question 2
Easy
Define hyperparameters.
💡 Hint: Consider what settings might affect how a model trains but aren't changed automatically during training.
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 advantage of AUC as an evaluation metric?
- It is easy to compute.
- It is threshold-independent.
- It provides a single value for accuracy.
💡 Hint: Think about what AUC tells us about model performance regardless of the threshold set.
Question 2
True or False: Increasing the complexity of a model generally prevents overfitting.
- True
- False
💡 Hint: Consider the balance between model fit and performance measurement.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Given a classification problem with highly imbalanced data, design a detailed analysis that compares performance using the ROC curve and Precision-Recall curve. What do you conclude?
💡 Hint: Think about which curve reveals more about the minority class performance.
Question 2
Select a dataset, conduct a hyperparameter tuning using both Grid Search and Random Search. Explain the differences in results and efficiency.
💡 Hint: Reflect on the significance of computational efficiency alongside performance.
Challenge and get performance evaluation