Glossary Library

Technical GlossaryMachine Learning

Elastic Net Regression

A regression method that combines L1 and L2 regularization to provide both coefficient shrinkage and partial feature selection.

Elastic Net regression is a hybrid regularization method that combines the strengths of Ridge and Lasso. It can perform especially well in high-dimensional datasets that contain correlated features. While the L1 penalty can suppress some variables, the L2 penalty stabilizes coefficient estimates. As a result, it is often valuable in practice for balancing generalization and interpretability.

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Regularization

A set of techniques used to reduce overfitting and improve a model’s ability to generalize.

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Mean Squared Error (MSE)

A common regression loss function that averages the squared differences between predictions and true values.

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Mean Absolute Error (MAE)

A regression loss function that averages the absolute differences between predictions and true values, offering greater robustness.