Regularization is a technique used to prevent overfitting in machine learning models by adding a penalty term to the loss function. The goal of regularization is to keep the model parameters from becoming too large, which can cause the model to fit the noise in the data instead of the underlying pattern.

L1 and L2 regularization are two commonly used types of regularization.

L1 regularization, also known as Lasso regularization, adds a penalty term to the loss function that is proportional to the absolute value of the coefficients. This has the effect of shrinking the coefficients towards zero, which can lead to sparse solutions, where some of the coefficients are exactly zero. In other words, L1 regularization encourages the model to use only a subset of the features. L2 regularization, also known as Ridge regularization, adds a penalty term to the loss function that is proportional to the square of the coefficients. This has the effect of shrinking the coefficients towards zero, but unlike L1 regularization, it does not encourage sparse solutions. L2 regularization tends to produce models with small, non-zero coefficients.

Simple Answer :  The main difference between L1 and L2 regularization is the way in which they penalize large coefficients. L1 regularization encourages sparse solutions, while L2 regularization discourages large coefficients, but does not encourage sparse solutions. The choice between L1 and L2 regularization depends on the problem at hand and the desired properties of the solution.