What is Overfitting?

Overfitting is when something learns too much from examples and can't handle new ones.

Imagine you're trying to guess what kind of fruit is in a basket just by looking at the fruits that come out one by one. At first, you might notice that all the apples are red, so you think "if it's red, it's an apple!" But then you see a red grape and get confused. That’s like overfitting, you focused too much on the color of the fruit instead of looking at other clues.

Think of a robot that learns to recognize cats by seeing many pictures of cats. It notices that all the cats have fur, four legs, and tails. But then it sees a picture of a cat wearing a hat, and it gets confused because it wasn’t expecting a hat on a cat.

That’s like when a learner pays attention to every detail, even ones that aren't really important. It works great with the examples they saw before, but it struggles with new ones because it's too focused on small details instead of seeing the big picture.

A good learner should notice what makes something special, like how a cat moves or looks, without getting distracted by things that might change, like hats or different kinds of fur. That way, they can handle both old and new examples easily.