Regularization (L1, L2)

Regularization (L1, L2)

Regularization is a type of model optimization used in machine learning that helps prevent Overfitting. Overfitting is a problem where a model becomes too attuned to the particularities of the training dataset, rather than generalizing to new, unseen data. Regularization helps combat this and can help models make accurate predictions.

Two common types of regularization are L1 and L2.

L1 Regularization

Also known as LASSO regression, L1 regularization is a technique that is used to reduce the complexity of a model. It does this by shrinking the weights associated with some of the parameters, which in turn removes infrequent, noisy, or irrelevant features from the model. This helps the model become more generalized and less prone to over-fitting. In addition, L1 regularization also helps to make feature selection easier.

L2 Regularization

Unlike L1 regularization, L2 regularization does not shrink the weights of some of the parameters. Instead, it adds a penalty term that penalizes large weights. In this way, L2 regularization encourages the weights to stay as small as possible, which in turn helps reduce the complexity of the model. Additionally, since the penalty term only penalizes large weights, it allows for non-linear models, which L1 regularization does not allow for.

Conclusion

Regularization helps to optimize machine learning models by preventing over-fitting and making feature selection easier. The two most common types of regularization are L1 and L2, which have slightly different approaches to reducing the complexity of the model. Using regularization techniques can help improve the accuracy and reliability of machine learning models.