Ridge regression

Imagine you are playing a fun game where you have to draw a line that goes through a bunch of dots on a piece of paper. The goal of the game is to draw the line that is as close to all the dots as possible.

Now imagine that the dots are really close together and it's hard to draw a line that goes through all of them perfectly. In this case, you might draw a line that is close to all the dots, but not exactly through each one.

That's kind of like what happens in ridge regression. Instead of trying to draw a line that goes through each point perfectly, we draw a line that is close to all the points, but not exactly through each one.

But why would we want to do that? Well, sometimes there are too many dots on our piece of paper and it's hard to draw a line that goes through all of them. In other words, our data has a lot of features or variables, and it's hard to come up with a simple equation that describes how they are related.

Ridge regression helps us deal with this problem by adding a little extra something to our equation to make it simpler. We call this extra something a "regularization term", and it helps our equation fit the data without being too complicated.

Think of it like this: if our equation was a person, ridge regression is like putting a backpack on that person to make them a little heavier. This backpack doesn't change the person's overall appearance, but it makes them a little more stable and harder to push around.

So in summary, ridge regression is a way to fit complicated data with a simple equation by adding a "backpack" (a regularization term) to make the equation more stable and less prone to overfitting.