Practice Linear Regression - 3 | Module 2: Supervised Learning - Regression & Regularization (Weeks 3) | Machine Learning
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3 - Linear Regression

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does the equation Y = Ξ²0 + Ξ²1X + Ο΅ represent?

πŸ’‘ Hint: Think about how predictions are made.

Question 2

Easy

What does R-squared measure?

πŸ’‘ Hint: Consider the importance of the independent variables' impact.

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 MSE stand for?

  • Mean Squared Error
  • Mean Standard Error
  • Minimum Squared Error

πŸ’‘ Hint: Think about how errors are squared in this metric.

Question 2

True or False: The slope in linear regression indicates the expected change in the dependent variable for a one-unit increase in the independent variable.

  • True
  • False

πŸ’‘ Hint: Remember the significance of the slope in the regression equation.

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You are trying to predict housing prices based on size, location, and number of bathrooms using multiple linear regression. However, you see that the coefficients for size and location are very high. What steps can you take to address potential multicollinearity?

πŸ’‘ Hint: Think about the relationships between your predictors.

Question 2

Consider a dataset where you apply polynomial regression. After fitting, you notice the model performs flawlessly on training data but poorly on testing data. What is happening here, and what steps could you take?

πŸ’‘ Hint: Visualize the trade-off between model complexity and data fit.

Challenge and get performance evaluation