Regularization (L1, L2) is an important concept in the field of machine learning and artificial intelligence that describes the process of introducing additional information and constraints into a machine learning model, in order to reduce the complexity of the model and improve the generalization of the model to unseen data.
Regularization, as the name suggests, involves the addition of “regularized” coefficients to a machine learning model, which can be thought of as constraints on how much influence a given parameter can have over the output of the model. The two most commonly used types of regularization are L1 and L2 regularization.
L1 regularization, also known as Lasso regularization, is based on the sum of the absolute values of the coefficients of the model. This type of regularization challenges the coefficients of the model, meaning that they can be reduced towards zero, which can result in a simpler and more parsimonious model.
L2 regularization, also known as Ridge regularization, is based on a different approach: it is based on the sum of the squared coefficients of the model. By adding a penalty to the squared coefficients, the model is forced to keep the coefficients small and close to zero, which results in a simpler and more parsimonious model.
Regularization is important because it allows us to reduce the complexity of the model without sacrificing accuracy when presented with unseen data. Additionally, it helps to reduce the risk of overfitting, which is when a model gets too complex and is unable to generalize well beyond the training data it was given.
Overall, regularization is a powerful tool that can help create more generalizable models for machine learning and artificial intelligence. It is an essential element when working with complex models and is used in many state-of-the-art machine learning solutions.
