The LASSO is a regularized regression approach commonly used for automated feature selection. LASSO penalizes the sum of the absolute size of the regression coefficients, forcing some of the regression coefficients to shrink to zero, thereby selecting a simpler and more interpretable model. The LASSO objective function can be written as:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathop {{\min }}\limits_{\beta _0,\beta } \frac{1}{N}\mathop {\sum }\limits_{i = 1}^N l(y_i,\beta _0 + \beta ^Tx_i) + \lambda \parallel \beta \parallel _1$$\end{document}minβ0,β1N∑i=1Nl(yi,β0+βTxi)+λ∥β∥1Where l is the negative log-likelihood function and λ is the regularization parameter.