Practice Supervised Learning - Regression & Regularization (2) - Supervised Learning - Regression & Regularization (Weeks 3)
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Supervised Learning - Regression & Regularization

Practice - Supervised Learning - Regression & Regularization

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is the primary purpose of linear regression?

💡 Hint: Think about what we want to achieve with regression.

Question 2 Easy

Define Mean Squared Error (MSE).

💡 Hint: Consider how we relate predictions to actual outcomes.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does Mean Squared Error (MSE) measure?

The average of squared errors.
The absolute error.
Both A and B.

💡 Hint: Think of how MSE is calculated in comparison to absolute error.

Question 2

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

True
False

💡 Hint: Consider the implications of adding unnecessary complexity.

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Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Consider data that exhibits a quadratic relationship. Design a study to apply polynomial regression, specifying the degree, and justify your choice.

💡 Hint: Think about the shape of your data when deciding on the polynomial degree.

Challenge 2 Hard

Evaluate a dataset for bias and variance issues. Provide plots and interpretations showing the model's performance.

💡 Hint: Visual plots will help clarify the concept of bias-variance trade-off effectively.

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Reference links

Supplementary resources to enhance your learning experience.