Lasso regularization is a technique used in regression analysis that adds a penalty equal to the absolute value of the magnitude of coefficients to the loss function. This approach encourages sparsity in the model by shrinking some coefficients to zero, effectively selecting a simpler model that helps prevent overfitting. By reducing complexity, lasso can improve the stability and conditioning of the model, making it more reliable in predictions and interpretations.
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