Lasso, which stands for "Least Absolute Shrinkage and Selection Operator," is a statistical method used primarily in regression analysis. It is particularly useful for feature selection and regularization when dealing with a large number of predictors in a regression model. Here's an overview of its key characteristics: 1. **Regularization**: Lasso adds a penalty term to the ordinary least squares (OLS) regression cost function. This penalty is proportional to the absolute values of the coefficients of the predictors.
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