Both L1 and L2 Regularization are used to prevent overfitting by penalizing the coefficients or weights. Below are the differences between L1 and L2 Regularization.

L1 Regularization |
L2 Regularization |

Lasso Regression | Ridge Regression |

Adds the absolute value of the coefficient as a penalty term to the loss function. | Adds the squared value of the coefficient as a penalty term to the loss function. |

Penalizes the sum of the absolute value of the weights. | Penalizes the sum of squares of weights. |

Performs Feature Selection. | Does not Perform Feature Selection. |

Robust to outliers. | Not robust to outliers. |

L = ∑(Ŷi– Yi)² + λ∑|β| |
L = ∑(Ŷi– Yi)² + λ∑β² |