← Library · Core concept
Overfitting
Overfitting occurs when a machine learning model learns the training data too well, memorizing noise and specific patterns that are not representative of the underlying data distribution. This leads to excellent performance on the training data but poor generalization to new, unseen data. The model becomes too complex for the problem it's trying to solve.
In plain terms
It's like studying for a test by memorizing every example question and answer word for word, rather than understanding the concepts, so you fail when new questions are asked.
Why it matters
It's a critical problem that prevents AI models from being useful in real-world scenarios by making them unreliable on new data.
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