Practice One-hot Encoding (2.3.4) - Data Wrangling and Feature Engineering

Practice: One-Hot Encoding

Lesson 2.3.4Data Wrangling and Feature Engineering

Practise this section

9 questions

Flashcard drill
4 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
Sign up to start the exercise
Quiz
3 questions · about 2 min · pass mark 70%. Hints available.
Sign up to start the quiz

6 sample questions

Question 1.
What is one-hot encoding?
Show hintHide hint
Think about how each category would be represented.
Question 2.
Why is one-hot encoding preferred over label encoding for nominal variables?
Show hintHide hint
Consider the meaning of categories.
3Question 3.
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
Show hintHide hint
Remember how it structures the data.
4Question 4.
One-hot encoding is best used for which type of categorical variable?
- True
- False
Show hintHide hint
Think about the nature of the categories.
Question 5.
Consider a dataset with a 'City' feature that has 50 unique city names. Discuss how you would encode this feature for a regression model.
Show hintHide hint
Think about dimensionality reduction strategies.
Question 6.
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?
Show hintHide hint
Consider what evaluation metrics would best reflect performance.

5 more questions in this section. Enroll free to practise them all.