Machine Learning

How to Develop Ridge Regression Models in Python

Last Updated on October 11, 2020

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 invokes adding penalties to the loss function during training that encourages simpler models that have smaller coefficient values. These extensions are referred to as regularized linear regression or penalized linear regression.

Ridge Regression is a popular type of regularized linear regression that includes an L2 penalty. This has the effect of shrinking the coefficients for those input variables that do not contribute much to the prediction task.

In this tutorial, you will discover how to develop and evaluate Ridge Regression models in Python.

After completing this tutorial, you will know:

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

Let’s get started.