Overfitting
Overfitting
A phenomenon where AI adapts too closely to training data, failing to generalize to new data.
In Simple Terms
Overfitting is a phenomenon where an AI adapts too closely to its training data, causing prediction accuracy to drop on new data. It happens during machine learning model training when the model picks up even fine-grained noise in the data. No matter how well the model performs on training data, it fails to function correctly in real-world use.
Behind the Name
The word "Overfitting" combines "Over" (meaning excessive) and "Fitting" (meaning to adjust or match). It captures the idea of an AI trying too hard to conform to its training data.
Take a Closer Look!
Overfitting is a phenomenon where an AI adapts too closely to the data it was trained on, making it unable to respond correctly to new, unseen data.
To put it simply, it's like an AI that scores perfectly in practice but falls apart when it really counts. Preventing this is an unavoidable challenge in building accurate AI systems.
The main causes of this phenomenon are insufficient training data or making the AI learn patterns that are overly complex.
The AI ends up treating even random noise in the data as a meaningful rule. When it then encounters new data, those meaningless rules cause it to make mistakes.
Common countermeasures include increasing the amount of training data or stopping the learning process partway through.
Another widely used approach is to prepare a separate set of test data — distinct from the training data — and regularly evaluate how well the model performs. The key is to help the AI identify shared, meaningful patterns rather than simply memorizing examples.