How to Develop Elastic Net Regression Models in Python

Regression is a modeling task that involves predicting a numeric value given an input.

Linear regression is the standard algorithm for regression that assumes a linear relationship between inputs and the target variable. An extension to linear regression involves adding penalties to the loss function during training that encourage simpler models that have smaller coefficient values. These extensions are referred to as regularized linear regression or penalized linear regression.

Elastic net is a popular type of regularized linear regression that combines two popular penalties, specifically the L1 and L2 penalty functions.

In this tutorial, you will discover how to develop Elastic Net regularized regression in Python.

After completing this tutorial, you will know:

• Elastic Net is an extension of linear regression that adds regularization penalties to the loss function during training.
• How to evaluate an Elastic Net model and use a final model to make predictions for new data.
• How to configure the Elastic Net model for a new dataset via grid search and automatically.

Let’s get started.