Regularization refers to a set of techniques used to prevent overfitting and improve the generalization of a model. Overfitting occurs when a model learns the training data too well, capturing noise and specific details that don't apply to new, unseen data. Regularization helps to control this by adding a penalty term to the model's objective function, discouraging overly complex models.
Two common types of regularization are L1 and L2 regularization:
L1 Regularization (Lasso Regression):
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L1 regularization adds a penalty term to the cost function equal to the absolute values of the model's coefficients.
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It encourages sparsity in the model by shrinking some coefficients to exactly zero, effectively performing feature selection.
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The resulting model is simpler and easier to interpret, as it selects only the most important features while discarding less relevant ones.
L2 Regularization (Ridge Regression):
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L2 regularization adds a penalty term to the cost function equal to the squared magnitudes of the model's coefficients.
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It tends to shrink the coefficients of less important features toward zero, but it rarely sets them exactly to zero.
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L2 regularization is effective in preventing overfitting by penalizing large weights and, therefore, reducing the model's complexity.
Both L1 and L2 regularization techniques help in reducing overfitting and improving the model's ability to generalize to unseen data. The choice between L1 and L2 regularization often depends on the specific problem, the nature of the features, and the desired outcome. Lasso (L1) regularization, with its feature selection property, is preferred when there is a need to identify the most relevant features. Ridge (L2) regularization is suitable when all features are potentially important and reducing their impact without eliminating them entirely is preferred. Additionally, a combination of both techniques, known as Elastic Net regularization, can be used to take advantage of both L1 and L2 regularization simultaneously.
