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Regularization is a technique used in supervised learning to prevent overfitting by adding a penalty to the model’s complexity. It helps improve the model’s generalization to unseen data and enhances its predictive performance.
Understanding Regularization
Regularization modifies the loss function during training by including a penalty term. Common types include L1 regularization (Lasso) and L2 regularization (Ridge). These methods control the magnitude of model parameters, encouraging simpler models.
Calculations Involving Regularization
In supervised learning, the regularized loss function can be expressed as:
Loss = Original Loss + λ × Regularization Term
Where λ (lambda) is the regularization parameter that determines the strength of regularization. For example, in Ridge regression, the regularization term is the sum of squared coefficients:
λ × Σ (coefficients)^2
Best Practices for Regularization
- Choose an appropriate regularization parameter (λ) through cross-validation.
- Start with small λ values and gradually increase to observe effects on model performance.
- Use L1 regularization when feature selection is desired.
- Combine regularization with feature scaling for better results.
- Monitor training and validation errors to avoid underfitting or overfitting.