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Lasso is a machine learning algorithm that is commonly used for feature selection and regularization. It stands for "Least Absolute Shrinkage and Selection Operator." Lasso works by adding a penalty term to the ordinary least squares regression model, which helps to prevent overfitting. The penalty term is based on the sum of the absolute values of the coefficients, thereby encouraging sparse solutions where some coefficients are set to zero.Lasso can be useful in situations where there are a large number of potential predictor variables and we want to identify the most important ones. By setting some coefficient estimates to zero, lasso effectively performs feature selection by excluding less important predictors from the model.In addition to feature selection, lasso can also be used for regularization, which helps to improve model generalization and reduce variance. By constraining the size of the estimated coefficients through the penalty term, lasso effectively shrinks them towards zero.One advantage of lasso over other regularization techniques like ridge regression is that it can produce sparse solutions. This means that it can directly eliminate irrelevant features from a model, making it easier to interpret and potentially reducing computation time.However, one limitation of lasso is that it tends to select only one variable from a set of highly correlated variables while ignoring the rest. This can result in instability in variable selection when there are many correlated predictors.Overall, lasso is a powerful tool in machine learning and statistics that can be used for both feature selection and regularization tasks. Its ability to produce sparse solutions makes it particularly useful when dealing with high-dimensional data or when interpretability is important.