What is the purpose of regularization in linear regression?

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Question: What is the purpose of regularization in linear regression?

Options:

Correct Answer: To reduce the risk of overfitting

Solution:

Regularization techniques, such as Lasso and Ridge regression, are used to reduce the risk of overfitting by penalizing large coefficients.

What is the purpose of regularization in linear regression?

What is the purpose of regularization in linear regression?

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.

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