Overfitting

Overfitting happens when an AI model memorizes its training data instead of learning generalizable patterns, leading to excellent training performance but poor real-world results. A classic example is a deep neural network that achieves 99% accuracy on training images but drops to 60% on new photos because it learned irrelevant background details rather than the actual objects. Overfitting is detected by comparing training accuracy to validation accuracy — when the gap widens, the model is overfitting. Common remedies include collecting more diverse data, regularization techniques like L1/L2 penalties and dropout, simpler model architectures, and early stopping during training. Overfitting is one of the most common failure modes in machine learning and a major AI risk management concern, particularly in regulated domains where models must generalize reliably. AI governance programs mandate validation, regularization, and ongoing monitoring to detect overfitting. Spotting overfitting early is essential for AI compliance and responsible AI deployment.

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