Description: Lasso regression adds L1 [[Data Science/Regularization]] to [[Linear Regression]] to perform feature selection by shrinking some coefficients to zero.
Key Points:
- Performs feature selection.
- Can produce sparse models.
- Requires tuning of the regularization parameter.
Applications: Gene selection, model selection, finance.