Practice - Module Objectives (for Week 8)
Practice Questions
Test your understanding with targeted questions
What does ROC curve measure?
💡 Hint: Think about what is being plotted on the axes of the ROC curve.
Name the two metrics that the Precision-Recall curve measures.
💡 Hint: These metrics relate to the classification of positive instances.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What does the ROC curve represent?
💡 Hint: Consider the axis labels.
True or False: AUC can be misleading in cases of class imbalances.
💡 Hint: Think about the implications of extreme class imbalance.
2 more questions available
Challenge Problems
Push your limits with advanced challenges
You have developed a model with an AUC of 0.6 on an imbalanced dataset. How do you plan to address the situation?
💡 Hint: Think about what AUC indicates in terms of model capability.
Given a validation curve for a hyperparameter, describe how you would identify the optimal value and risk of overfitting.
💡 Hint: Visual aids can help clarify the transition point in your analysis.
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Reference links
Supplementary resources to enhance your learning experience.
- Understanding ROC Curves and AUC
- Precision and Recall: What’s the Difference?
- Hyperparameter Tuning with Grid Search
- Hyperparameter Tuning with Random Search
- Learning Curves - Understanding Model Performance
- Validation Curve Visualization in Scikit-Learn
- Evaluating Model Performance: A Focus on Precision, Recall, and F1 Score