What is L2 regularization (Ridge)?

L2 regularization, also called Ridge, is like giving your math teacher a gentle nudge so they don’t go overboard with big numbers.

Imagine you're trying to guess how many jellybeans are in a jar. You and your friend both make guesses, but your teacher says you can only use small numbers, not too high or too low. That helps keep your answers simple and fair. Ridge works like that: it gently tells the model (like you) to not use really big numbers when making predictions, so it stays balanced.

Think of a backpack full of books. If you pack too many heavy books, your back hurts, just like if a model uses too big numbers, it can get confused. Ridge is like adding a soft cushion to the bag: it doesn’t stop you from packing, but makes it easier on your back.

Sometimes models try to be too perfect with their answers. They might use huge numbers just to fit all the details. But that can make them messy or not work well with new problems. Ridge helps keep things tidy, like how a soft cushion keeps your backpack from hurting too much.