Regularization Techniques in Machine Learning

Regularization in machine learning is a powerful technique we can use to prevent overfitting. In this article we’re going to explore what regularization is, how it works and describe different types.

What is regularization?

Regularization is a technique that introduces a penalty term to the loss function. This is necessary to prevent the model from learning noise in the dataset. Therefore, the model will learn underlying patterns in the data rather than memorize it.

This helps improve the generalization of the model, which is then able to perform better on new unseen data.

How does it work?

As we already mentioned, it introduces a penalty term to the loss function while model is training. This penalty term is usually a function of the model’s weights, which encourages the model to learn simpler patterns.

In other words, it makes changes we make to weights less optimal in order to make a model become more generalizable.

Types of regularization

L1 regularization

L1 or Lasso regularization adds a penalty term which is proportional to the absolute value of the weights. It also encourages the model to set some of the weights to zero, effectively removing some of the features from the model.

We can use it for feature selection, where our model learns only the most important features.

L2 regularization

L2 or Ridge regularization adds a penalty term which is proportional to the square of the weights. Furthermore, it encourages the model to learn small weight values, which smoothes out the model’s decision boundary.

This type is useful for reducing the impact of noisy and/or irrelevant features.

How to implement it?

We can implement it by adding the penalty term to the loss function and adjust the hyperparameter that controls the strenght of the regularization. Furthermore, this hyperparameter determines the tradeoff between fitting the training data well and avoiding overfitting.

We can find the optimal value for this hyperparameter by using techniques such as cross-validation.


In conclusion, it is a powerful technique we use to prevent overfitting in machine learning. By adding a penalty term to the loss function, we can control how well our model can learn patterns from the data.

By understanding the different types and how to implement them, we can create more robust and accurate machine learning models.

I hope this article helped you gain a better understanding about regularization techniques and maybe even motivate you to learn more about it.

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