regularization

L1 and L2 Regularization Methods

A regression model that uses L1 regularization technique is called Lasso Regression and model which uses L2 is called Ridge Regression. The key difference between these two is the penalty term. Lasso shrinks the less important feature’s coefficient to zero

Artificial Intelligence

Regularization is a method used to reduce the variance of your model and increase the bias. It is used when your model overfits the training data. Another method to do regularization is called Lasso regression. This is the solution to Biase-Variance Dilemma.