Elastic Net Regression is a regularization technique that combines the approaches of Lasso and Ridge regression. Here are some key points about Elastic Net:
Like Lasso, it performs automatic variable selection by driving some coefficients to zero.
But similar to Ridge, it also handles groups of correlated predictors by combining both L1 and L2 penalties.
The regularization term is a linear combination of L1 and L2 norms: (1-α)L2 + αL1
α tunes the relative contribution of L1 vs L2 penalty between 0-1.
α=1 recovers Lasso, α=0 recovers Ridge regression.
It overcomes limitations of Lasso by allowing groups of correlated features to be selected together.
Performs better than Lasso in situations with highly correlated features.
The grouping effect makes coefficients more stable and parameter estimation consistent even with numerous predictors.
Useful as a compromise between sparsity of Lasso and grouping effect of Ridge regularization.
Hyperparameters like α and lambda need to be tuned for best performance.
So in summary, Elastic Net achieves sparsity and grouping effect simultaneously, making it a flexible regression model for high-dimensional variable selection.
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