Overfitting

Overfitting happens when an AI model learns the training data too well, including the noise, mistakes, or random patterns that do not actually matter. As a result, the model performs very well on the data it was trained on but struggles when faced with new, unseen data. It is like a student who memorizes every word of a textbook but does not really understand the subject and fails the exam when questions are phrased differently.

To avoid overfitting, developers use techniques like simplifying the model, adding more diverse training data, or using something called regularization. The goal is to create a model that learns the general patterns in data, not just memorizes the answers. A well-balanced model should perform well on both training data and real-world data.