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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is the primary purpose of regularization in machine learning?
💡 Hint: Think about what happens when a model learns too much detail from training data.
Question 2
Easy
Name two types of regularization techniques.
💡 Hint: Think naming conventions that indicate their behavior with model coefficients.
Practice 4 more questions and get performance evaluation
Interactive Quizzes
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What does Elastic Net regularization combine?
- L1 and L2 penalties
- Only L1 penalties
- Only L2 penalties
💡 Hint: Remember the two types it uses.
Question 2
True or False: Cross-validation can lead to overfitting.
- True
- False
💡 Hint: Think about how it evaluates across different data partitions.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Given a dataset with 10 predictors, some of which are correlated, describe a strategy for using Elastic Net to select features while preventing overfitting.
💡 Hint: Consider how Elastic Net allows for both shrunk coefficients and feature elimination.
Question 2
Create a Python script that performs Elastic Net regression on a given dataset, and utilize K-Fold cross-validation to evaluate its performance.
💡 Hint: Focus on setting up appropriate data preprocessing before the implementation.
Challenge and get performance evaluation