L2 regularization

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.


  • 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