Technical GlossaryMachine Learning

Lasso Regression

An L1-based regression method that can perform both regularization and feature selection by driving coefficients to zero.

Lasso regression is a powerful method used especially in high-dimensional datasets to reduce model complexity and select informative features. Because of its L1 penalty, it can drive some coefficients exactly to zero, effectively removing variables from the model. This improves interpretability while reducing the influence of unnecessary features. However, because it may behave unstably among highly correlated predictors, its results should be interpreted within the broader problem context.