AI & Technology

What Is Overfitting in Machine Learning?

Overfitting happens when a machine learning model learns its training data too closely — including noise and random quirks — so it performs very well on that data but poorly on new data. The model has memorized rather than learned patterns that generalize.

How It Is Detected

A large gap between accuracy on training data and accuracy on separate test data is the classic sign.

Overfitting vs. Underfitting

Underfitting is the opposite: the model is too simple to capture the real pattern. Techniques such as using more data, simpler models, and regularization help find the balance.

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🔗 Related Terms

Training Data Machine Learning Linear Regression Standard Deviation

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Last updated: September 29, 2026. This definition is for general educational purposes only. It is not financial, investment, tax, legal, or medical advice, and it does not recommend any product or course of action. For decisions about your own situation, consult a qualified professional.