Data Science Numerical Analysis
Lasso regression is a statistical method used for linear regression that incorporates regularization to enhance prediction accuracy and interpretability. By adding a penalty equal to the absolute value of the magnitude of coefficients, lasso regression encourages sparsity in the model, effectively shrinking some coefficients to zero and leading to simpler models. This technique is particularly useful when dealing with high-dimensional data, where many predictors may be irrelevant.
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