After understanding the basic principles of linear regression and gradient descent, It is time to move forward a bit and review some techniques that improve the performance of ordinary linear regression models. The most common techniques are LASSO regularization (L1 Regularization) and Ridge Regularization (L2 Regularization). First, we need to know what “Regularization” means. Simply Regularization is the process of adding information to prevent over-fitting.

The over-fitting problem occurs when the error of the model is minimum in the training phase, but the performance of the model with testing data points is poor. …

Bassem Essam

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store