What is a common technique to prevent overfitting in linear regression models?
- Increasing the model complexity
- Reducing the number of features
- Regularization
- Using a smaller training dataset
Regularization is a common technique used to prevent overfitting in linear regression models. It adds a penalty term to the linear regression's cost function to discourage overly complex models. Regularization techniques include L1 (Lasso) and L2 (Ridge) regularization.
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