Lasso regression
Lasso regression is a type of Linear regression that uses L1 regularization to reduce the magnitude of model coefficients, effectively setting some coefficients to zero. This technique is useful for Feature selection and preventing Overfitting. The lasso regression algorithm is commonly used in Data analysis and Machine learning to identify the most important Predictor variables in a dataset. It was introduced by Robert Tibshirani in 1996 as a way to improve the accuracy of Linear models. Lasso regression is often compared to Ridge regression, which uses L2 regularization. The choice between lasso and ridge regression depends on the specific problem and the desired level of Model complexity.