```
tags:
- AI/Regularization
aliases:
- Mean Squared Error (MSE)
- Least Squared Error (LSE)
- L2 Loss
```

Is used for Regression Tasks, It tells you how close a regression line is to a set of data points.

- Where
determines the amount of regularization. - Mathematically it adds the sum of squares of coefficients to the cost function.
- L2 Regularization penalizes larger Weights more severely (due to the squared penalty term), which makes weight values to lean toward zero.

Notes:

- Calculating derivation is easier in L2 Regularization .
- L2 Regularization is more sensitive to outliers due to using the square difference.
- A Linear Regression that uses the L2 regularization technique is called Ridge Regression.
- L2 Regularization addresses Multicollinearity by constraining the coefficient norm

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