<ul><li>LASSO is a technique that combines linear regression with L1 regularization to automatically select relevant features and produce sparse models.</li><li>The optimization function of LASSO minimizes the sum of squared residuals while constraining the sum of absolute weights, forcing many coefficients to zero.</li><li>L1 regularization in LASSO creates a constraint region in parameter space, essential for feature selection by forcing some coefficients to exactly zero.</li><li>LASSO is useful for feature importance analysis, robust to multicollinearity, and can be visualized through the regularization path of coefficients.</li></ul>

Understanding the Magic of LASSO Feature Selection

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