Overfitting - Page 6

Overfitting is a common issue in machine learning where a model learns the training data too precisely, capturing noise and minor details instead of general patterns. As a result, the model performs exceptionally well on the data it was trained on but poorly on new, unseen data. This happens when the model is too complex, uses too many parameters, or lacks sufficient regularization. Techniques such as cross-validation, dropout, and early stopping are used to prevent overfitting and improve a model’s ability to generalize. Addressing overfitting is crucial for building reliable AI systems that perform consistently across real-world scenarios.