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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is an imbalanced dataset?
💡 Hint: Think about how many instances you have of each class.
Question 2
Easy
Name one metric used for evaluating imbalanced datasets.
💡 Hint: Consider metrics that focus on positives.
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 a key metric in evaluating imbalanced datasets?
- Accuracy
- Recall
- F1-Score
💡 Hint: Think about metrics that account for both false positives and negatives.
Question 2
True or False: Accuracy is always a reliable metric for model evaluation.
- True
- False
💡 Hint: Consider situations where one class vastly outnumbers another.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Given a dataset with two classes, A (90% samples) and B (10% samples), how would you evaluate a model trained on this data? What metrics would you focus on and why?
💡 Hint: Consider the implications of performance metrics when classes are imbalanced.
Question 2
Explain how you would implement SMOTE in a practical case and discuss its potential pitfalls.
💡 Hint: Think about balancing quantities and avoiding mimicking too closely.
Challenge and get performance evaluation