Q-113. Regularization is an approach for ensuring that models do not become too large or complex, and it is particularly useful when there is a large number of highly correlated features. Two widely-used regularization methods are known as the ridge and the LASSO (the least absolute shrinkage and selection operator). Which of the following statements correctly describe these two methods and their applications?

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The LASSO is known as the L1 regularization because of the first-order natures of the shrinkage term, and it is used to reduce the magnitude of the parameters, making them closer to, but not equal to, zero.

The ridge is known as the L1 regularization because of the first-order natures of the shrinkage term, and it is used to set some of the less-important parameter estimates to zero.

The LASSO is known as the L2 regularization because of the second-order natures of the shrinkage term, and it is used to remove less important features.

The ridge is known as the L2 regularization because of the second-order natures of the shrinkage term, and it helps avoid situations where the parameter estimates are offsetting, with one having a large positive value and another a large negative value.

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