## What is Elastic-net? Why is it better in comparison to Ridge and Lasso?

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

Machine Learning Interview Questions

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

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

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

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

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

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