lasso regression

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lasso regression. It is very good to use for statics processing.

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Lasso regression is a statistical technique that is commonly used in data analysis to obtain a model that is parsimonious and interpretable. It is a type of linear regression that performs both variable selection and regularization by adding a penalty term to the ordinary least squares (OLS) objective function. This penalty term shrinks the coefficients of the less important variables to zero, allowing us to identify the most relevant features in the data. In addition, lasso regression is particularly useful when we have a large number of features and we want to avoid overfitting the model. Overall, lasso regression is an effective tool for processing statistics and has numerous practical applications in fields such as economics, social sciences, and engineering.