Machine Learning Intermediate 1 min read

What is L2 regularization?

A technique to prevent overfitting in machine learning models by adding a penalty term to the loss function

L2 regularization explained in plain English

L2 regularization is a method used to reduce the complexity of a machine learning model by limiting the magnitude of its weights, which helps to prevent overfitting and improve generalization

Analogy

L2 regularization is like a speed limit on a highway, it prevents the model from going too fast and making reckless decisions, by slowing it down and making it more cautious

Example

A company building a predictive model to forecast sales, uses L2 regularization to prevent the model from becoming too complex and fitting the noise in the training data, resulting in better predictions on new data

How is L2 regularization used?

L2 regularization is used in machine learning algorithms such as linear regression, logistic regression, and neural networks to improve their performance and prevent overfitting

Common misconceptions about L2 regularization

A common misconception is that L2 regularization always improves the performance of a model, however, it can also lead to underfitting if the regularization strength is too high

History

L2 regularization, also known as Ridge regression, was first introduced by Hoerl and Kennard in 1970 as a method to improve the estimation of regression coefficients

Also known as

Ridge regression weight decay Tikhonov regularization