Overfitting is a problem in
artificial intelligence where an
AI model learns the
training data (
datasets) with too much precision, including noise and exceptions, causing it to perform well with that data but poorly with new data.
Imagine a student who memorizes the exact answers from previous exams instead of understanding the concepts. When faced with new but similar questions, they fail because they haven't
learned to generalize. This same situation occurs with
AI models that suffer from overfitting.
In technical terms, an overfitted model has captured specific
patterns and noise from the
training data that don't represent the general reality. For example, an image recognition model might
learn to identify cats based on irrelevant details that appeared in the
training photos, such as a watermark or a certain background.
To combat overfitting, data scientists use techniques such as cross-validation (testing the model with data it hasn't seen during
training), regularization (penalizing excessive model complexity), or data augmentation (creating more varied examples for
training). The goal is to achieve a balance: a model complex enough to capture important
patterns, but not so much that it "memorizes" the
training data.