Regularization is a technique used in machine learning to prevent overfitting and improve the generalization of a model. Overfitting occurs when a model is too complex and fits the training data too closely, resulting in poor performance on new, unseen data.
Regularization works by adding a penalty term to the cost function of the model. The penalty term is based on the magnitude of the model's parameters, and it encourages the model to choose simpler solutions that generalize better to new data.
There are two main types of regularization: L1 regularization and L2 regularization. L1 regularization adds a penalty term proportional to the absolute value of the model's parameters, while L2 regularization adds a penalty term proportional to the square of the model's parameters.
Regularization is important because it helps to prevent overfitting, which can lead to poor performance on new data. By adding a penalty term to the cost function, the model is encouraged to choose simpler solutions that generalize better to new data. Regularization is especially useful when working with high-dimensional data, where overfitting is a common problem.
In addition, regularization can also help to improve the interpretability of the model. By encouraging the model to choose simpler solutions, the model's parameters become more sparse and easier to interpret. This can be especially useful in applications where understanding the underlying relationships between variables is important.