Practice Lab: Implementing and Evaluating Various Regression Models, Including Polynomial Regression - 4 | Module 2: Supervised Learning - Regression & Regularization (Weeks 3) | Machine Learning
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4 - Lab: Implementing and Evaluating Various Regression Models, Including Polynomial Regression

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

Define Simple Linear Regression.

πŸ’‘ Hint: Think about its components - how many variables are involved?

Question 2

Easy

What does MSE measure?

πŸ’‘ Hint: Consider what happens to errors when squared.

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 the term 'MSE' stand for in regression metrics?

  • Mean Squared Error
  • Mean Standard Error
  • Median Squared Error

πŸ’‘ Hint: It’s a commonly used metric in regression.

Question 2

True or False: Increasing the degree of a polynomial regression model always improves its performance.

  • True
  • False

πŸ’‘ Hint: Consider how the model behaves with unseen data.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Given a dataset that shows a clear quadratic relationship, what degree of polynomial regression would be most appropriate, and why?

πŸ’‘ Hint: Consider the shape of the data when plotting.

Question 2

You’ve trained a high-degree polynomial model but observe poor performance on test data. What steps can you take to improve your model?

πŸ’‘ Hint: What adjustments can you make to decrease overfitting?

Challenge and get performance evaluation