{"id":7453,"date":"2024-02-13T14:00:52","date_gmt":"2024-02-13T20:00:52","guid":{"rendered":"https:\/\/www.sharpsightlabs.com\/?p=7453"},"modified":"2024-02-13T15:32:30","modified_gmt":"2024-02-13T21:32:30","slug":"regularization-explained","status":"publish","type":"post","link":"https:\/\/www.sharpsightlabs.com\/blog\/regularization-explained\/","title":{"rendered":"Machine Learning Regularization, Explained"},"content":{"rendered":"

Dealing with the problem of overfitting is one of the core issues in machine learning and AI.<\/p>\n

Your model seems to work perfectly<\/em> on the training set, but when you try to validate it on the test set … it’s terrible.<\/p>\n

This is a core problem in machine learning, especially when you start using advanced ML techniques like deep learning.<\/p>\n

And to solve the problem of overfitting, you often need regularization<\/strong>.<\/p>\n

Regularization is one of the primary tools that we use to build models that fit the data well, but also generalize well to unseen data.<\/p>\n

So in this blog post, I’m going to explain the essentials of regularization.<\/p>\n

I’ll explain what regularization is and also introduce you to the primary types of regularization.<\/p>\n

If you need something specific, you can click on any of the links below, and the link will take you to the appropriate location in the article.<\/p>\n

Table of Contents:<\/strong><\/p>\n