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Overfitting

Overfitting is the phenomenon where a machine learning model memorizes its training data instead of learning general patterns. The result: excellent performance on already-seen data, poor performance on new data, like a student who memorizes exercise solutions without understanding the method.

Definition

It is one of the fundamental problems in machine learning and is countered with several techniques: more training data, regularization, validation on held-out data, early stopping. Its opposite is underfitting: a model too simple to capture the real patterns.

The concept is also useful for those who use AI without training it: when a company fine-tuning job is done poorly, the model can "overfit" to the examples provided and lose flexibility on everything else.

Related terms

• Machine Learning
• Training Data
• Fine-tuning
• Deep Learning
• Parameters

More in AI Fundamentals

• LLM (Large Language Model)
• Token
• Context Window
• Temperature
• AI Hallucination
• Fine-tuning
• RAG (Retrieval Augmented Generation)
• Embedding

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