Regularization in statistics is a technique used to prevent overfitting in predictive models, especially in the context of machine learning and regression analysis. Overfitting occurs when a model fits the training data very closely but fails to generalize well to new, unseen data. Regularization introduces a penalty term into the model’s error function, discouraging it from learning overly complex relationships in the data.
There are two common types of regularization:
- L1 Regularization (Lasso): L1 regularization adds a penalty to the absolute values of the model’s coefficients. It encourages some coefficients to become exactly zero, effectively performing feature selection. This means it can eliminate less important features from the model, leading to a simpler and more interpretable model.
- L2 Regularization (Ridge): L2 regularization adds a penalty to the squares of the model’s coefficients. It doesn’t force coefficients to be exactly zero but discourages them from growing to very large values. This helps control the complexity of the model and prevent overfitting.
Regularization is like adding a constraint to the model’s optimization process. It encourages the model to find a balance between fitting the training data well and keeping the model simple enough to generalize to new data. Regularization is a powerful tool to improve the robustness and performance of machine learning models, especially when dealing with high-dimensional data or limited data samples.