Elastic Net Regularization
A statistical technique that combines L1 and L2 penalties of the lasso and ridge methods to improve model accuracy and interpretability, especially in datasets with correlated variables.
Implications
A statistical method used in regression models that combines L1 and L2 regularization techniques to prevent overfitting by penalizing large coefficients, particularly useful in models with many correlated variables.
Example
Example: A data scientist uses elastic net regularization to build a predictive model for customer churn, balancing between selecting important features and minimizing the model's complexity.
Related Terms
Different from Lasso (L1) or Ridge (L2) regularization alone, elastic net regularization combines both to handle situations where there are many correlated predictors.
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