elastic net regularization
Elastic net regularization is a regularization technique that combines the penalties of both Lasso and Ridge regression to prevent overfitting in linear regression models. It involves adding a penalty term that is a mix of the L1 (Lasso) and L2 (Ridge) penalties to the coefficient estimates. This helps with feature selection and handling multicollinearity, striking a balance between the two types of regularization.
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