Practice Linear & Polynomial Regression (2.1) - Supervised Learning - Regression & Regularization (Weeks 3)
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Linear & Polynomial Regression

Practice - Linear & Polynomial Regression

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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is the equation for simple linear regression?

💡 Hint: Think of Y as the value you want to predict.

Question 2 Easy

Name one advantage of using polynomial regression over linear regression.

💡 Hint: Consider scenarios where data trends change direction.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

Which method is used to update parameters in a regression model to minimize errors?

Stochastic Gradient Descent
Batch Gradient Descent
Both
Neither

💡 Hint: Think about how progressive updates can occur in gradient descent.

Question 2

True or False: Higher R-squared values always indicate a better model.

True
False

💡 Hint: Consider whether all metrics need to be examined for model performance.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

You are given a dataset representing house prices over time. Describe how you would approach building both a linear regression model and a polynomial regression model and the considerations involved in each.

💡 Hint: No hint provided

Challenge 2 Hard

Explain why the bias-variance trade-off is crucial when developing predictive models and give practical solutions for managing this trade-off.

💡 Hint: No hint provided

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