Practice - Lab: Implementing and Evaluating Various Regression Models, Including Polynomial Regression
Practice Questions
Test your understanding with targeted questions
Define Simple Linear Regression.
💡 Hint: Think about its components - how many variables are involved?
What does MSE measure?
💡 Hint: Consider what happens to errors when squared.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What does the term 'MSE' stand for in regression metrics?
💡 Hint: It’s a commonly used metric in regression.
True or False: Increasing the degree of a polynomial regression model always improves its performance.
💡 Hint: Consider how the model behaves with unseen data.
1 more question available
Challenge Problems
Push your limits with advanced challenges
Given a dataset that shows a clear quadratic relationship, what degree of polynomial regression would be most appropriate, and why?
💡 Hint: Consider the shape of the data when plotting.
You’ve trained a high-degree polynomial model but observe poor performance on test data. What steps can you take to improve your model?
💡 Hint: What adjustments can you make to decrease overfitting?
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Reference links
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