1. What is the primary purpose of regularization in machine learning models?
ANSWER= C)
Explain:- The primary purpose of regularization is to prevent overfitting by penalizing large coefficients.
2. Which type of regularization adds the absolute value of the magnitude of coefficients to the loss function?
ANSWER= A)
Explain:- L1 regularization, also known as Lasso, adds the absolute value of the magnitude of coefficients to the loss function, which can lead to sparse models.
3. Which of the following is a characteristic of L2 regularization?
ANSWER= D)
Explain:- L2 regularization, also known as Ridge, adds the squared magnitude of coefficients to the loss function, which prevents overfitting by reducing the model complexity but does not encourage sparsity.
4. In Elastic Net regularization, which parameters control the balance between L1 and L2 regularization?
ANSWER= D)
Explain:- In Elastic Net regularization, Alpha controls the overall strength of regularization, while L1_ratio controls the balance between L1 (Lasso) and L2 (Ridge) regularization.
5. What effect does increasing the alpha parameter in Lasso regression have on the model?
ANSWER= B)
Explain:- Increasing the alpha parameter in Lasso regression increases the penalty on large coefficients, which decreases the model complexity and can lead to sparser models.
6. Which of the following is not a form of regularization?
ANSWER= D)
Explain:- Cross-validation is a technique used for model validation, not regularization. Dropout, batch normalization, and early stopping are forms of regularization.
7. What is the primary purpose of dropout regularization in neural networks?
ANSWER= B)
Explain:- Dropout regularization prevents neurons from co-adapting too much by randomly dropping units during the training process, which helps to reduce overfitting.
8. Which technique is commonly used to find the optimal value of the regularization parameter?
ANSWER= C)
Explain:- Cross-validation is commonly used to find the optimal value of the regularization parameter by evaluating the model's performance on different subsets of the training data.
9. What is the primary difference between L1 and L2 regularization in terms of their effect on the model coefficients?
ANSWER= A)
Explain:- L1 regularization encourages sparsity by driving some coefficients to zero, while L2 regularization tends to shrink coefficients but does not drive them to zero.
10. How does regularization help in improving the generalization ability of a machine learning model?
ANSWER= A)
Explain:- Regularization improves the generalization ability of a machine learning model by reducing its variance, which helps to prevent overfitting to the training data.
