Practice Label Encoding - 2.3.5 | 2. Data Wrangling and Feature Engineering | Data Science Advance
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2.3.5 - Label Encoding

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is label encoding?

💡 Hint: Think about how machines understand data.

Question 2

Easy

Is label encoding suitable for nominal data?

💡 Hint: Consider what happens with ordered 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 label encoding do?

  • Converts categorical variables to audio signals
  • Converts categorical variables to numeric values
  • Excludes categorical variables from analysis

💡 Hint: Think about the role of data in machine learning.

Question 2

Is label encoding suitable for nominal data?

  • True
  • False

💡 Hint: Remember the difference between ordinal and nominal.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You have a dataset containing categorical variable 'Education' with values 'High School', 'Bachelor', and 'Master'. Discuss how you would apply label encoding here and explain the reasoning behind your approach.

💡 Hint: Consider the implications of the educational levels on future processes.

Question 2

Suppose you're given a dataset with a nominal variable 'Color' that includes 'Red', 'Blue', and 'Green'. What would be the challenges of applying label encoding here, and how could that affect your model?

💡 Hint: Think about how categories relate (or don’t relate) to each other.

Challenge and get performance evaluation