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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
Define what 'K' represents in KNN.
💡 Hint: Think about how many neighbors the model looks at to make its decision.
Question 2
Easy
What happens if you set 'K' to a very small number?
💡 Hint: Consider how sensitive the model will be to noise.
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 a small value of 'K' usually indicate?
- High bias
- High variance
- Low bias
- Stable performance
💡 Hint: Consider how much the model relies on individual data points.
Question 2
True or False: Larger values of 'K' generally increase bias while reducing variance.
- True
- False
💡 Hint: Reflect on the implications of averaging predictions among more neighbors.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You are given a dataset with a high number of features. Describe how increasing dimensions might impact the choice of 'K' and suggest methods to address this.
💡 Hint: Consider how distance measures may become ineffective as dimensions rise.
Question 2
After choosing 'K', you notice that your KNN model performs poorly on unseen data. What steps would you take to reassess your choice of 'K'?
💡 Hint: Why might you adjust your approach based on validation scores?
Challenge and get performance evaluation