This article is about Lasso Regression and Ridge Regression or other call it **L1 and L2 regularization**, here we will learn and discuss **L1 vs L2 Regularization Guide: Lasso and Ridge Regression.**

The key difference between **L1 and L2 regularization** is the penalty term or how weights are used, L2 is the sum of the square of the weights, while L1 is just the absolute sum of the weights, using these techniques we can to avoid over-fitting.

## L1 Regularization or Lasso Regression

In **L1 Regularization** or **Lasso Regression**, the cost function is changed by **L1 loss function **which used to minimize the error, that is the sum of the all the **absolute**(mod) differences between the actual value and the predicted value.

**L2 Regularization or Ridge Regression**

In **L2 Regularization** or **Ridge Regression**, the cost function is changed by **L2 loss function** which used to minimize the error, that is sum of the all the **squared** differences between the actual value and the predicted value.

### End Notes:

1. **Lasso Regression** is useful for feature selection as regression is performed by removing the slopes whose value after model fitting is approaching to zero, meaning they are less important to the model.

2. It is very important to choose right value of lambda, otherwise model can lead to under-fit.