What is Overfitting
Overfitting is a modeling error in machine learning where an algorithm learns the training data too specifically, including noise and random fluctuations, resulting in poor performance on new, unseen data. When a model overfits, it memorizes training examples rather than learning generalizable patterns, leading to high accuracy on training data but low accuracy on validation or test datasets. This occurs when models are too complex relative to the amount of training data available, or when training continues for too long. Common signs include a large gap between training and validation performance metrics.
Overfitting reduces model reliability and limits real-world applicability. Prevention techniques include regularization, cross-validation, early stopping, dropout layers, and data augmentation. Understanding and preventing overfitting is crucial for building robust AI systems that perform consistently across different datasets and deployment environments.
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