How to Develop LARS 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.
Lasso Regression is a popular type of regularized linear regression that includes an L1 penalty. This has the effect of shrinking the coefficients for those input variables that do not contribute much to the prediction task.
Least Angle Regression or LARS for short provides an alternate, efficient way of fitting a Lasso regularized regression model that does not require any hyperparameters.
In this tutorial, you will discover how to develop and evaluate LARS Regression models in Python.
After completing this tutorial, you will know:
- LARS Regression provides an alternate way to train a Lasso regularized linear regression model that adds a penalty to the loss function during training.
- How to evaluate a LARS Regression model and use a final model to make predictions for new data.
- How to configure the