lasso regression
时间: 2023-11-14 22:07:31 浏览: 71
Lasso regression, also known as L1 regularization, is a linear regression technique that adds a regularization term to the cost function. The regularization term is the L1 norm of the coefficients, which forces some of them to be exactly zero. This helps in feature selection, as features that are not important for predicting the output are set to zero, resulting in a simpler and more interpretable model.
The main advantage of lasso regression over other regularization techniques such as ridge regression (L2 regularization) is that it can lead to sparse models, where only a subset of the features is used for prediction. However, a disadvantage of lasso is that it may not perform well if the number of features is larger than the number of observations, as it may select too few features and lead to underfitting.
Lasso regression is widely used in machine learning and statistics for various applications, including image processing, genetics, finance, and natural language processing.