But What Is Overfitting in Machine Learning?

Overfitting is when a machine learning model learns too much from its training data and fails to work well on new data.

Imagine you're trying to draw a picture of your favorite animal, let's say it's a cat, based only on what you see in the morning. If you look at the same cat every day, you might start drawing extra details like the exact pattern of fur or even the way its whiskers bend in the light. But if someone shows you a different cat later, your picture might not match it at all because you focused too much on the specific details of one cat.

Think of a model that learns too many tricks to pass a test. It memorizes every question and answer in its training set, but when new questions come up, it doesn’t know how to solve them, it's like having a friend who knows all the answers by heart but can't explain the math behind them.

Say you're trying to guess your friend’s favorite number based on what they tell you. If they only give you one number, and you remember that exactly, you might get it right, but if they ask about another number next time, you might be stuck. That's like overfitting, learning too much from a small set of examples.