Description

Overfitting occurs when a statistical model or machine learning algorithm captures the noise of the data rather than the underlying pattern. An overfitted model performs well on training data but poorly on unseen data, indicating poor generalization.

Examples

• 📊 High accuracy on training data but low accuracy on test data
• 🔍 Complex decision boundaries in simple classification tasks

Applications