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Answer: All the above.
## Explanation Regularization techniques are indeed used to prevent overfitting and manage model complexity, especially when dealing with highly correlated features. The options provided represent: - **A. Ridge regression**: Also known as L2 regularization, it adds a penalty term proportional to the square of the coefficients to the loss function. - **B. LASSO**: Stands for Least Absolute Shrinkage and Selection Operator (L1 regularization), which adds a penalty term proportional to the absolute value of coefficients and can perform feature selection by driving some coefficients to zero. - **C. A hybrid of Ridge regression and LASSO**: This refers to Elastic Net regularization, which combines both L1 and L2 penalties. Since all three options (Ridge, LASSO, and their hybrid Elastic Net) are valid regularization techniques, the correct answer is **D. All the above**.
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Regularization is an approach for ensuring that models do not become too large or complex, and it is particularly useful when there is a considerable number of highly correlated features. Regularization techniques including:
A
Ridge regression.
B
LASSO.
C
A hybrid of the Ridge regression and LASSO.
D
All the above.
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