Overfitting

In more detail

Overfitting occurs when a model memorizes training data rather than learning generalizable patterns. It has low training loss but high validation loss. Solutions include regularization, dropout, early stopping, and using more diverse training data.

Examples

• A model that memorizes training images but fails on new images
• High training accuracy but low test accuracy
• Detecting overfit through validation curves

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