```
tags:
- AI/Tasks/Regression
- AI/ML/SupervisedLearning
- AI/Regression
- AI/Regularization
aliases:
- Least Absolute Shrinkage and Selection Operator
- Lasso Regression Algorithm
```

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.

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