### What is Regularization?

Regularization involves adding a penalty for complexity to the model objective function to improve a modelâ€™s generalization performance.

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Regularization involves adding a penalty for complexity to the model objective function to improve a modelâ€™s generalization performance.

L1 regularization, or LASSO (Least Absolute Shrinkage and Selection Operator), is a kind of regularization

L2, or Ridge regularization, is a form of regularization in which the penalty is based on the squared magnitude of the coefficients.

If a primary interest is to conduct automatic variable selection, only LASSO can do that.

LASSO performs feature selection by shrinking the coefficients of variables to zero

Elastic net uses a weighted combination of the L1 and L2 penalties that are used in both LASSO and Ridge regression, respectively.

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- Machine Learning 101 (30)
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