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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is the purpose of deletion in handling missing values?
💡 Hint: Think about what happens when you remove data.
Question 2
Easy
Describe mean imputation.
💡 Hint: What do we calculate to find the 'mean'?
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 the primary method for filling in small amounts of missing data?
- Deletion
- KNN
- MICE
- Mean Imputation
💡 Hint: Think about removing minimal versus large amounts of missing information.
Question 2
True or False: Mean imputation can introduce bias if the data distribution is skewed.
- True
- False
💡 Hint: Consider how averages behave in skewed distributions.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You have a dataset with 20% missing values in a feature. When considering deletion and imputation, what factors would influence your decision?
💡 Hint: Reflect on the balance between data quantity and quality.
Question 2
Design an experiment where you use KNN for imputation, outlining your dataset, chosen neighbors, and the method to evaluate accuracy.
💡 Hint: Remember to tune the number of neighbors for optimal performance.
Challenge and get performance evaluation