Participate in this quiz to evaluate your understanding of regularization, a fundamental technique in machine learning that helps to prevent overfitting and improve the generalizability of models.

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**Machine Learning Quizzes**

Participate in this quiz to evaluate your understanding of regularization, a fundamental technique in machine learning that helps to prevent overfitting and improve the generalizability of models.

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- Question 1 of 10
##### 1. Question

**What happens to the coefficients in Ridge Regression as the regularization parameter approaches zero?**CorrectIncorrect - Question 2 of 10
##### 2. Question

**In the context of regularization, under which circumstance would you prefer to use Elastic Net over LASSO and Ridge?**CorrectIncorrect - Question 3 of 10
##### 3. Question

**In the context of regularization, under which circumstance would you prefer to use LASSO over Ridge?**CorrectIncorrect - Question 4 of 10
##### 4. Question

**In which scenario would you prefer using Ridge Regression over LASSO?**CorrectIncorrect - Question 5 of 10
##### 5. Question

**How does increasing the regularization parameter in LASSO affect the bias and variance of the model?**CorrectIncorrect - Question 6 of 10
##### 6. Question

**What is a potential drawback of using a very strong LASSO regularization (very high lambda)?**CorrectIncorrect - Question 7 of 10
##### 7. Question

**What is the geometric interpretation of the regularization term in Ridge Regression when viewed as a constraint in the optimization problem?**CorrectIncorrect - Question 8 of 10
##### 8. Question

**What is the geometric interpretation of the regularization term in LASSO Regression when viewed as a constraint in the optimization problem?**CorrectIncorrect - Question 9 of 10
##### 9. Question

**In Elastic Net, the mixing parameter (denoted as “l1_ratio”) controls the trade-off between L1 and L2 regularization. What is the range of values for the mixing parameter?**CorrectIncorrect - Question 10 of 10
##### 10. Question

**Consider a scenario where you are applying LASSO Regression to a dataset with highly correlated features. How does the introduction of L1 regularization affect the behavior of the model in terms of selecting features?**CorrectIncorrect

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