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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 of it as turning categories into 0s and 1s.
Question 2
Easy
What does Label Encoding do?
💡 Hint: Consider it a way to give each category a rank.
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 One-Hot Encoding?
- A method to combine features
- Converts categorical data into binary values
- Assigns numerical values to categories
💡 Hint: Think about how categories are represented in matrices.
Question 2
True or False: Label Encoding can imply an artificial order in nominal data.
- True
- False
💡 Hint: Remember the difference between nominal and ordinal data.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Given a dataset with a feature 'City' that includes 'New York', 'Los Angeles', and 'Chicago', apply One-Hot Encoding and explain the implications for model interpretation.
💡 Hint: Visualize how each city needs to be treated distinctly in a model.
Question 2
You have a dataset containing 'Size' with values 'Small', 'Medium', and 'Large'. How would you encode this using Label Encoding, and what might be the drawback?
💡 Hint: Consider how each size might not truly indicate a rank.
Challenge and get performance evaluation