Elastic Net Regression is a powerful tool for machine learning problems with many features or predictors. This method combines the benefits of both Ridge Regression and Lasso Regression to handle datasets effectively. In this lesson, we'll explore what Elastic Net Regression is and compare it with Linear Regression, Ridge Regression, and Lasso Regression using Python's Scikit-Learn library. By the end of this lesson, you'll understand how to create and interpret an Elastic Net Regression model and compare its performance with other regression techniques.

Have you ever tried drawing a straight line through points on a graph but found the data too noisy or complex? Linear Regression might not always work well, especially with datasets having many features. Here's where Elastic Net Regression comes in to save the day.

Elastic Net Regression combines two popular regularization techniques: Ridge Regression and Lasso Regression. Regularization helps to prevent overfitting, which happens when your model memorizes the training data too well, making it perform poorly on new data.

Let's break down two important parameters of Elastic Net Regression:

1. Alpha ($\alpha$): This controls the overall strength of the regularization. A higher value means more regularization.