Overfitting

A training error where an AI model becomes too specialised to its training data, performing excellently on familiar examples but failing to generalise to new, unseen situations. Overfitted models memorise specific patterns rather than learning general principles, limiting their practical business value. For organisations implementing AI, overfitting represents a significant risk that can result in AI systems that work well in testing but fail in real-world deployment. Preventing overfitting requires careful training practices, diverse datasets, proper validation techniques, and ongoing monitoring to ensure AI systems maintain performance across varying business conditions.