Regularization algorithms are techniques used in machine learning to prevent overfitting of a model to the training data. Overfitting occurs when a model is too complex and captures noise and randomness in the training data, resulting in poor generalization to new, unseen data.
Regularization algorithms work by adding a penalty term to the cost function that the model tries to minimize during training. The penalty term is designed to encourage the model to have smaller weights or coefficients, which in turn results in a simpler model that is less likely to overfit. The penalty term is typically based on either the L1 norm (Lasso regularization) or the L2 norm (Ridge regularization) of the weights or coefficients.
Lasso regularization encourages the model to have sparse weights, meaning that it will only use a subset of the available features for prediction, while Ridge regularization tends to distribute the weights more evenly across all the features. Elastic Net regularization is a combination of both Lasso and Ridge regularization.
Regularization algorithms are particularly useful when working with high-dimensional data where there are many features relative to the number of samples. They are commonly used in linear regression, logistic regression, and neural network models.