Lasso Regression

Lasso regression, or Least Absolute Shrinkage and Selection Operator, is a linear regression technique that incorporates L1 regularization. This regularization penalizes big coefficients, resulting in a sparse model with certain coefficients becoming zero. Lasso regression is advantageous for variable selection and preventing overfitting by simplifying complex modes.

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