Introducing regularization

“Regularization in ML is a technique used to improve the generalization performance of a model by adding additional constraints to the model’s parameters. This forces the model to use simpler representations and helps reduce the risk of overfitting.

Regularization can also help improve the performance of a model on unseen data by encouraging the model to learn more relevant, generalizable features.”

This definition of regularization, arguably good enough, was actually generated by the famous GPT-3 model when given the following prompt: Detailed definition of regularization in machine learning. Even more astonishing, this definition passed several plagiarism tests, meaning it’s actually fully original text. Do not worry if you do not yet understand all the words in this definition from GPT-3; it is not meant for beginners. But you will fully understand it by the end of this chapter.

Note

GPT-3, short for Generative Pre...