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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is one-hot encoding?
💡 Hint: Think about how each category would be represented.
Question 2
Easy
Why is one-hot encoding preferred over label encoding for nominal variables?
💡 Hint: Consider the meaning of categories.
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 one-hot encoding do?
- It converts categorical variables into integer labels
- It converts categorical variables into binary columns
- It removes categorical variables from the dataset
💡 Hint: Remember how it structures the data.
Question 2
One-hot encoding is best used for which type of categorical variable?
- True
- False
💡 Hint: Think about the nature of the categories.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Consider a dataset with a 'City' feature that has 50 unique city names. Discuss how you would encode this feature for a regression model.
💡 Hint: Think about dimensionality reduction strategies.
Question 2
A dataset includes a variable 'Vehicle Type' with values: Car, Truck, SUV, and Motorcycle. How would you evaluate the impact of one-hot encoding on a prediction model’s performance?
💡 Hint: Consider what evaluation metrics would best reflect performance.
Challenge and get performance evaluation