From the course: Deep Learning with Python: Optimizing Deep Learning Models

Elastic Net regularization - Python Tutorial

From the course: Deep Learning with Python: Optimizing Deep Learning Models

Elastic Net regularization

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- [Instructor] Elastic net regularization combines the penalties of both L one and L two regularization making it especially useful when dealing with data where some features are highly correlated or when neither L one nor L two regularization alone provides optimal results. The loss function for elastic net re regularization is defined as shown here where alpha controls the overall strength of the regularization and Rho is a mixing parameter between L one and L two regularization. Values for Rho between zero and one create a combination of both L one and L two. However, when Rho equals one, the effect will be the same as L one or lasso regularization, and when Rho equals zero, the effect will be the same as L two regularization. Essentially, elastic net regularization aims to leverage the benefits of both L one and L two regularization, by encouraging sparsity like L one for feature selection, ensuring that the model only uses the most relevant features and stabilizing the model like L two by penalizing large weights uniformly preventing any single weight from dominating. This also reduces the risk of overfitting. Choosing to use elastic net regularization over L one and L two regularization depends on the specific requirements of the problem at hand. Elastic net is particularly well suited for situations where the number of features is much larger than the number of observations. Elastic Net can help select the most relevant subset of features without completely ignoring correlated ones. It also is useful when the dataset has groups of correlated features. L one regularization may arbitrarily select one feature from a correlated group potentially ignoring useful information. L two regularization, on the other hand, tends to include all features, but doesn't shrink on important weights to zero. Elastic net balances these behaviors allowing for group feature selection while still maintaining weight decay. The role parameter in elastic net offers fine tuned control over the regularization balance, allowing for a flexible combination of the feature selection properties of L one with the stability and weight distribution of L two.

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