What is the purpose of regularization in linear regression?
Practice Questions
1 question
Q1
What is the purpose of regularization in linear regression?
To increase the number of features
To reduce the risk of overfitting
To improve the interpretability of the model
To ensure normality of residuals
Regularization techniques, such as Lasso and Ridge regression, are used to reduce the risk of overfitting by penalizing large coefficients.
Questions & Step-by-step Solutions
1 item
Q
Q: What is the purpose of regularization in linear regression?
Solution: Regularization techniques, such as Lasso and Ridge regression, are used to reduce the risk of overfitting by penalizing large coefficients.
Steps: 7
Step 1: Understand that linear regression is a method used to predict outcomes based on input data.
Step 2: Know that sometimes, linear regression can fit the training data too closely, which is called overfitting.
Step 3: Realize that overfitting means the model performs well on training data but poorly on new, unseen data.
Step 4: Learn that regularization is a technique used to prevent overfitting.
Step 5: Recognize that regularization adds a penalty for large coefficients in the model.
Step 6: Identify two common types of regularization: Lasso (which can reduce some coefficients to zero) and Ridge (which shrinks coefficients but keeps all of them).
Step 7: Conclude that the purpose of regularization is to create a simpler model that generalizes better to new data.
