Overfitting
When a model memorises training examples too precisely and fails to generalise to new data.
Plain English Explanation
Overfitting happens when a system memorises its training examples too precisely and fails on new data. It performs brilliantly on what it has seen before but struggles when faced with slightly different situations.
Good models balance fitting the training data with remaining flexible enough for the real world.
Analogy
Overfitting is like a student who memorises every past exam paper word for word but cannot answer a question phrased slightly differently. They mastered the examples, not the subject.
How is it used?
A language model that quotes training data verbatim instead of generalising. A photo classifier that only recognises images taken in one specific lighting condition. Any AI that aces tests but fails in the real world.
Real-world Example
An hiring model trained on past decisions may overfit to historical bias and reject qualified candidates who look different from previous hires.
Common Misconceptions
High accuracy on training data does not mean a model is good — it may have simply memorised rather than learned.