Lasso regularization works by adding a penalty term to the loss function of the regression model. The modified loss function for linear regression can be expressed as:
Here, \( \lambda \) is the regularization parameter that controls the strength of the penalty, \( N \) is the number of observations, \( y_i \) are the observed values, \( X_i \) are the predictor values, and \( \beta \) are the coefficients. By tuning \( \lambda \), researchers can control the trade-off between model complexity and prediction accuracy.
