Practice - Week 4: Regularization Techniques & Model Selection Basics
Practice Questions
Test your understanding with targeted questions
What is overfitting?
💡 Hint: Think about how you could memorize the whole training set.
What does Lasso Regularization do?
💡 Hint: Consider how it affects the number of features used.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What is the purpose of regularization in machine learning?
💡 Hint: Think about what regularization is known for.
True or False: L1 regularization can lead to some feature coefficients being precisely zero.
💡 Hint: Consider how Lasso affects feature selection.
2 more questions available
Challenge Problems
Push your limits with advanced challenges
You have two models: Model A shows signs of overfitting, while Model B shows underfitting. Propose a regularization strategy for each model and justify your choices.
💡 Hint: Think about the nature of the problems each model is facing.
Differentiate the results obtained through K-Fold cross-validation vs a simple train/test split in a project. Provide a detailed analysis discussing reliability measures.
💡 Hint: Consider the validity of performance assessments.
Get performance evaluation
Reference links
Supplementary resources to enhance your learning experience.