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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
Define bias in machine learning.
💡 Hint: Think about the oversimplification of reality.
Question 2
Easy
What does high variance indicate?
💡 Hint: Consider how the model responds to minor changes.
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 bias represent in a model?
- Error from overfitting
- Error from oversimplification
- Error from high variance
💡 Hint: Think about how simplification affects understanding.
Question 2
True or False: High variance models always perform better than low variance models.
- True
- False
💡 Hint: Consider the difference between training and unseen data performance.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You have two models: Model A with high bias and Model B with high variance. Your dataset has a complex underlying structure. Which model would you prefer if your goal is to generalize well? Provide reasoning.
💡 Hint: Consider what each model distinctly can learn from the complexity.
Question 2
Suppose you are working on a classification problem with imbalanced data (many instances of one class and very few of another). How does this scenario affect your bias-variance trade-off, and what measures could you take?
💡 Hint: Reflect on how class representation influences learning.
Challenge and get performance evaluation