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:
Let’s get started.
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