Decision-tree pruning

Okay kiddo, let me explain decision-tree pruning to you! Imagine you have a really big tree with lots and lots of branches, but some of those branches don't really help you anymore. It's kind of like having a big pile of toys in your room, but you don't play with some of them anymore.

So, what do you do? You prune the tree! This means you cut off some of the branches that aren't helping you anymore, or you get rid of some of the unused toys from your pile. When we talk about decision trees, pruning means we remove some of the branches from the tree that don't give us any useful information.

Why do we prune decision trees? Well, we want to make sure our tree is simple and accurate. If we have too many branches or rules, it can be hard to understand our decision tree and it may not work well for predictions. So, we prune the tree to make it easier to understand and more accurate.

Now, how do we decide which branches to prune? This is where things get a little more complicated, but I'll try to explain it in a simple way. We look at each branch and ask ourselves, "Does this branch help me make better predictions?" If the answer is no, we remove that branch.

But wait, there's more! We also have to be careful not to remove too many branches, or we might lose important information that could help us make good predictions. It's kind of like taking away too many toys from your pile - you might accidentally get rid of your favorite toy!

So, to sum it up, decision-tree pruning is like cleaning up a messy toy pile, but instead of removing toys, we remove branches from our decision tree. We want to make sure our tree is simple and accurate, but we also don't want to remove important information. Does that make sense, kiddo?