Lasso

Lasso (Least Absolute Shrinkage and Selection Operator) is a statistical method used to improve the accuracy of models by selecting only the most important variables. It adds a penalty based on the absolute value of the coefficients, which can shrink some coefficients to exactly zero. This means Lasso effectively performs feature selection, simplifying the model by excluding less relevant variables. It's useful when dealing with many variables, helping prevent overfitting and making the model more interpretable. Overall, Lasso balances fitting the data well while keeping the model as simple and relevant as possible.