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 |