Practice Data Preparation and Initial Review - 4.2.1 | Module 2: Supervised Learning - Regression & Regularization (Weeks 4) | Machine Learning
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is the purpose of data preprocessing?

πŸ’‘ Hint: Think about how data is prepared for machine learning models.

Question 2

Easy

Define the test set in a machine learning context.

πŸ’‘ Hint: Remember, it should not be used during training!

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 purpose of data preprocessing?

  • To reduce dataset size
  • To prepare data for analysis
  • To create new features

πŸ’‘ Hint: Why do we clean and adjust the data before analysis?

Question 2

True or False: The test set should be used during model training.

  • True
  • False

πŸ’‘ Hint: Consider which data should guide the model's learning process.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You have access to a dataset containing housing prices but find that several features contain missing values. Describe the steps you would take to prep this data for modeling, addressing missing values and potential outliers.

πŸ’‘ Hint: Consider the importance of each step and why it matters.

Question 2

Reflect on a scenario where your linear regression model shows high performance on training data but poor performance on test data. What steps would you consider to address this issue?

πŸ’‘ Hint: Think about potential causes and solutions systematically.

Challenge and get performance evaluation