What is regularization?

Regularization is like helping your friend tidy up their room so they can find what they need faster.

Imagine you and your friend are trying to solve a puzzle. Your friend has too many toys scattered everywhere, it’s hard for them to focus on the puzzle pieces because everything is messy. Regularization is like asking your friend to put away some of the extra toys, so there's more space for the important stuff, like the puzzle.

In real life, this happens when we’re trying to make predictions using data. Sometimes, our model uses too many details from the data, it’s like having too many toys in the room. This makes it harder for the model to focus on what really matters. Regularization helps by simplifying things, so the model doesn’t get distracted by unnecessary parts.

Think of it like this: if you're trying to draw a picture, but someone keeps adding extra details that don't help, like drawing tiny dots all over the sky, your picture might look more complicated than it needs to be. Regularization is like saying, "Let’s keep it simple and beautiful."

Regularization helps models make better predictions by keeping things clean and focused.