What is Regularization?
Regularization involves adding a penalty for complexity to the model objective function to improve a model’s generalization performance.
Regularization involves adding a penalty for complexity to the model objective function to improve a model’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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