Which of the following techniques can help prevent overfitting in linear regress

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Question: Which of the following techniques can help prevent overfitting in linear regression?

Options:

Correct Answer: Using regularization techniques like Lasso or Ridge

Solution:

Regularization techniques like Lasso or Ridge can help prevent overfitting by adding a penalty for larger coefficients.

Which of the following techniques can help prevent overfitting in linear regression?

Which of the following techniques can help prevent overfitting in linear regression?

Step 1: Understand what overfitting means. Overfitting happens when a model learns the training data too well, including noise, and performs poorly on new data.
Step 2: Learn about linear regression. Linear regression is a method used to predict a target variable by fitting a line to the data points.
Step 3: Know that regularization is a technique used to prevent overfitting. It adds a penalty to the model for having large coefficients.
Step 4: Familiarize yourself with Lasso and Ridge regression. Lasso adds a penalty based on the absolute value of coefficients, while Ridge adds a penalty based on the square of coefficients.
Step 5: Realize that by using Lasso or Ridge, you can keep the model simpler and more general, which helps it perform better on new data.

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