Lasso (knot)

Lasso, short for Least Absolute Shrinkage and Selection Operator, is a statistical technique used to improve the accuracy of models that predict outcomes based on many features. It works by adding a constraint that shrinks some of the model's coefficients toward zero, effectively reducing the influence of less important variables. This process helps in selecting the most relevant features, leading to simpler and more interpretable models while reducing overfitting. Think of Lasso as a way to "penalize" complexity, encouraging models to focus on the most significant predictors for more reliable predictions.