Practice - Final Unbiased Evaluation (on the Held-Out Test Set)
Practice Questions
Test your understanding with targeted questions
What does overall accuracy measure in a machine learning model?
💡 Hint: Consider the correctness of all predicted labels.
Define precision in the context of model evaluation.
💡 Hint: Think about how correct positives relate to all positive predictions.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What does the ROC curve represent?
💡 Hint: Think about which rates relate to classification performance.
True or False: AUC of 0.5 suggests a model performs better than random guessing.
💡 Hint: Recall what AUC signifies about model discrimination ability.
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Challenge Problems
Push your limits with advanced challenges
You have a highly imbalanced dataset where the positive class occurs only 5% of the time. Describe how you would approach evaluating a model trained on this data.
💡 Hint: Consider which evaluation metrics are more informative in imbalanced cases.
After evaluating your model on a held-out test set, you notice a significant drop in recall compared to your validation set. What might be the reasons for this, and how would you investigate further?
💡 Hint: Reflect on how data distribution impacts model performance.
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Reference links
Supplementary resources to enhance your learning experience.