A regression model that uses L1 Regularization is called Lasso Regression, and the Model which uses L2 Regularization is called Ridge Regression.
* L1 regularization helps in eliminating the features that are not important.
* L1 regularization adds the penalty term in the cost function by adding the absolute value of weight (Wj), while L2 regularization adds the squared value of weights (Wj) in the cost function.
* One more difference between both of them is that L1 regularization tries to estimate the median of the data while L2 regularization tries to estimate the mean of the data.
