Lasso Regression

Least Absolute Shrinkage and Selection Operator (LASSO) is the process of shrinking or regularizing to avoid Overfitting to minimize prediction error.

It adds a penalty to basic regression procedure(It's Cost Function), allowing the model to work as a Regularization algorithm by introducing some amount of bias in the model which lowers Overfitting.

Notes:

• Lasso Regression allows Feature Selection by penalizing the absolute values of regression coefficients and allowing some of the coefficients to reach absolute zero.
• Lasso Regression often uses L1 Regularization for penalty.