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Knowledge
Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does overall accuracy measure in a machine learning model?
💡 Hint: Consider the correctness of all predicted labels.
Question 2
Easy
Define precision in the context of model evaluation.
💡 Hint: Think about how correct positives relate to all positive predictions.
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 ROC curve represent?
- Trade-off between precision and recall
- Trade-off between true positive rate and false positive rate
- Accuracy of the model
💡 Hint: Think about which rates relate to classification performance.
Question 2
True or False: AUC of 0.5 suggests a model performs better than random guessing.
- True
- False
💡 Hint: Recall what AUC signifies about model discrimination ability.
Solve and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
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.
Question 2
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.
Challenge and get performance evaluation