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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is bias in machine learning?
💡 Hint: Think about how assumptions affect a model's performance.
Question 2
Easy
What happens when a model has high variance?
💡 Hint: Consider how a model might react to noise in the dataset.
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 high bias in a model lead to?
- Overfitting
- Underfitting
- Good performance
💡 Hint: Think about what happens when a model doesn't learn enough.
Question 2
True or False: High variance is always desirable in a model.
- True
- False
💡 Hint: Consider the draw of complexity vs. performance.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You are tasked with building a model to predict sales for a new product. Describe how you would approach addressing both bias and variance in your model design.
💡 Hint: Consider starting with basic features and then introduce new ones carefully.
Question 2
A model shows excellent training accuracy but poor validation accuracy. Discuss the adjustments you’d implement to address high variance.
💡 Hint: Think about how you can reduce model complexity.
Challenge and get performance evaluation