Practice Module Objectives (for Week 4) (2) - Supervised Learning - Regression & Regularization (Weeks 4)
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Module Objectives (for Week 4)

Practice - Module Objectives (for Week 4)

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is overfitting?

💡 Hint: Think about the difference between learning and memorizing.

Question 2 Easy

What does L1 regularization do?

💡 Hint: Consider how it selects features.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does overfitting indicate about a model's performance?

It performs well on training data but poorly on unseen data
It performs poorly on both training and test data
It performs equally well on both datasets

💡 Hint: Think about the difference between memorizing answers and understanding concepts.

Question 2

True or False: Regularization techniques can only be applied to linear regression models.

True
False

💡 Hint: Consider the diversity of machine learning algorithms.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

You are designing a model where you have a dataset with 100 features. How would you decide between using Lasso, Ridge, or Elastic Net regularization?

💡 Hint: Assess your feature's relevance and correlations.

Challenge 2 Hard

During a cross-validation process, your model's performance fluctuates greatly between folds. What steps would you take to stabilize these estimates?

💡 Hint: Think about your dataset size and distribution.

Get performance evaluation

Reference links

Supplementary resources to enhance your learning experience.