Practice Module 2: Supervised Learning - Regression & Regularization (weeks 4) (1)
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Module 2: Supervised Learning - Regression & Regularization (Weeks 4)

Practice - Module 2: Supervised Learning - Regression & Regularization (Weeks 4)

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is overfitting?

💡 Hint: Think about how well it performs on unseen data.

Question 2 Easy

List one cause of underfitting.

💡 Hint: Consider how complex the data is.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the purpose of L1 regularization?

To minimize the absolute values of coefficients
To minimize the squared values of coefficients
To remove irrelevant features

💡 Hint: Consider what happens to features with small importance.

Question 2

True or False: L2 regularization is also known as Ridge regression.

True
False

💡 Hint: Think about the naming of regression techniques.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Given a dataset with multicollinearity, discuss how you would decide between Lasso and Ridge regression, and justify your answer.

💡 Hint: Think about the nature of your features and their relationships.

Challenge 2 Hard

You have a dataset that is both large and complex. Explain your choice of regularization method and validation technique.

💡 Hint: Consider the model's flexibility and evaluation accuracy.

Get performance evaluation

Reference links

Supplementary resources to enhance your learning experience.