Ever felt like you studied too much for an exam, cramming in all the tiny details from the textbook, but then totally blanked out when faced with the real test? That’s pretty much what overfitting is in the AI world. Overfitting is when our AI system, let’s call him Marvin, becomes an ‘overachiever’. Marvin memorizes the entire training data set down to the last detail, but he then struggles when faced with fresh, unseen data. It’s like if Marvin learned to ride a bicycle in the park with precise turns and maneuvers but ends up puzzled when asked to ride in an unfamiliar territory, like the bustling city street.
On the flip side, underfitting is Marvin’s little blip in learning. It’s when he just skims the surface of the training data and doesn’t learn enough. Consequently, he performs poorly not only on new data but also on the training data itself. It’s akin to Marvin trying to ride a bike but never quite mastering the balance or the pedaling – he just isn’t going to get far in the park or the city street.
The idea is to strike a balance – Marvin needs to learn to ride his bike well enough in the park but also adapt to the busy city streets. That’s where proper AI modeling and adjustment come into play, to prevent both overfitting and underfitting for a smooth ride in the AI world.