Practice One-Hot Encoding - 2.3.4 | 2. Data Wrangling and Feature Engineering | Data Science Advance
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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