Lasso (Least Absolute Shrinkage and Selection Operator)

Lasso is a statistical method used to improve the accuracy of predictive models and simplify them by shrinking some of the model's coefficients toward zero. Think of it as a way to identify the most important factors influencing an outcome by penalizing less relevant variables. When coefficients are reduced to zero, those variables are effectively excluded from the model, helping to avoid overfitting and making the model easier to interpret. This technique is especially useful when dealing with many variables, as it finds a balance between fitting the data well and keeping the model simple.