How do Ridge and Lasso regularization techniques interact with Polynomial Regression to mitigate overfitting?
- By adding a penalty term to constrain coefficients
- By fitting low-degree polynomials
- By ignoring interaction terms
- By increasing the model's complexity
Ridge and Lasso regularization techniques mitigate overfitting in Polynomial Regression by adding a penalty term to the loss function. This constrains the coefficients, reducing the complexity of the model, and helps in avoiding overfitting.
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