Regularization Techniques for Preventing Overfitting

Learn How to Use Regularization in Machine Learning to improve the generalization performance of a model

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Regularization is a technique used in machine learning to prevent overfitting and improve the generalization performance of a model. Overfitting occurs when a model is too complex and learns the noise in the training data instead of the underlying patterns. Regularization adds a penalty term to the loss function to discourage the model from learning small, irrelevant details in the training data.

There are several popular regularization techniques, including L1 (Lasso) regularization, L2 (Ridge) regularization, and Elastic Net. In this article, we will discuss these techniques and show how to implement them in Python.

Let’s discuss these techniques one by one and learn how we can implement them in Python.

L1 (Lasso) Regularization

L1 regularization adds a penalty term equal to the absolute value of the coefficients to the loss function. This makes the optimization problem more difficult and discourages the model from learning coefficients that are not important. The L1 penalty is proportional to the magnitude of the coefficients, so it tends to drive some of them to…