Regularization methods for dummies

Machine learning can seem like a daunting field, especially when you’re faced with complex terms and techniques.

Fear not! In this article, we’ll break down regularization methods, specifically LASSO, Ridge, and similar techniques, into simple, easy-to-understand terms so that even beginners can grasp the basics.

What is Regularization and Why Do We Need It?

Regularization is a technique used in machine learning and statistics to prevent overfitting. Overfitting occurs when a model learns to perform extremely well on the training data but struggles to generalize its predictions to new, unseen data.

Essentially, the model becomes too complex and fails to capture the underlying patterns in the data.

Regularization works by adding a penalty term to the loss function, which encourages the model to find simpler solutions that are more likely to generalize well to new data.

Regularization Methods

There are several regularization methods, and we’ll be focusing on three popular ones: LASSO, Ridge, and Elastic Net.

LASSO (Least Absolute Shrinkage and Selection Operator)

LASSO is a regularization method that encourages sparse solutions, meaning it tries to set some of the model’s parameters (or weights) to zero. This has the effect of selecting only the most important features for the model, thus simplifying it.

In LASSO, we add an L1 penalty term to the loss function. The L1 penalty is the sum of the absolute values of the model’s parameters. By controlling the strength of this penalty, we can influence how many parameters are set to zero, and therefore, the complexity of the model.

Ridge Regression

Ridge regression is another popular regularization method. Instead of using an L1 penalty like LASSO, it uses an L2 penalty, which is the sum of the squared values of the model’s parameters. This encourages the model to distribute the importance of features more evenly, resulting in a more balanced model.

Ridge regression doesn’t typically set parameters to zero but instead shrinks them towards zero. This means that, unlike LASSO, Ridge regression doesn’t perform feature selection but rather reduces the impact of less important features.

Elastic Net

Elastic Net is a regularization method that combines aspects of both LASSO and Ridge regression. It does this by including both L1 and L2 penalty terms in the loss function.

This allows the model to perform feature selection like LASSO while also distributing the importance of features more evenly like Ridge regression.

Elastic Net is particularly useful when dealing with datasets where there is a high degree of multicollinearity, meaning some features are highly correlated with one another.

In such cases, LASSO may perform poorly, while Ridge regression may still struggle with feature selection.

Conclusion

Regularization is a powerful technique for preventing overfitting in machine learning models. LASSO, Ridge, and Elastic Net are three popular regularization methods, each with its unique strengths and weaknesses.

By understanding how these methods work and when to apply them, you can build more robust models that perform well on both training and unseen data.