Practice Encoding Categorical Data - 5.4 | Chapter 5: Data Preprocessing for Machine Learning | Machine Learning Basics
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5.4 - Encoding Categorical Data

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is the main purpose of encoding categorical data?

💡 Hint: Think about what type of data machine learning algorithms work best with.

Question 2

Easy

Can you explain OneHotEncoding?

💡 Hint: Remember how categories are transformed into separate columns.

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 OneHotEncoding used for?

  • To convert text to integers
  • To create binary columns for categories
  • To scale numerical values

💡 Hint: Think about how categories are represented in a dataset.

Question 2

True or False: LabelEncoding is always the best choice for categorical variables.

  • True
  • False

💡 Hint: Consider the nature of the data you are encoding.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Suppose you have a dataset containing countries and a product rating ('Good', 'Average', 'Bad'). Outline an approach for encoding this dataset to prepare it for a machine learning model.

💡 Hint: Consider the nature of the ratings and how they should influence model training.

Question 2

Given a dataset of survey responses including 'Yes', 'Sometimes', 'No' as answers, suggest an encoding strategy that maintains the options’ intrinsic order while encoding them for a model.

💡 Hint: Think about how to maintain the order when encoding.

Challenge and get performance evaluation