Practice Linear Regression Baseline (without Regularization) (4.2.3) - Supervised Learning - Regression & Regularization (Weeks 4)
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Linear Regression Baseline (Without Regularization)

Practice - Linear Regression Baseline (Without Regularization)

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is the purpose of creating a baseline model in regression?

💡 Hint: Think about how we compare new models.

Question 2 Easy

Define Mean Squared Error (MSE).

💡 Hint: What does MSE reflect about model accuracy?

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the primary purpose of establishing a baseline model?

To achieve the highest accuracy possible
To compare with future models
To eliminate noise

💡 Hint: Consider how we measure improvements in model performance.

Question 2

True or False: A high R-squared value always indicates a good model fit.

True
False

💡 Hint: Think critically about R-squared in context.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Suppose after running your baseline model, you discover a very low training MSE and a high test MSE. Discuss potential strategies to improve the model's generalization ability.

💡 Hint: Think about how to modify the model for better adaptability.

Challenge 2 Hard

You have a dataset with multiple features. Explain how each feature's significance might affect the outcome of the baseline regression model.

💡 Hint: Evaluate the impact of each predictor.

Get performance evaluation

Reference links

Supplementary resources to enhance your learning experience.