Lassoing

Lassoing, in the context of data analysis, refers to a statistical technique called Lasso regression. It's used to select important variables in a model by adding a penalty term that shrinks some coefficients toward zero. This process effectively eliminates less significant variables, leading to simpler, more interpretable models. Think of it as trimming unnecessary predictors, helping to improve prediction accuracy and reduce overfitting, especially when dealing with many variables. Lasso regression balances model complexity and fit, making it a valuable tool in statistical modeling and machine learning.