{"version":"1.0","type":"rich","provider_name":"gaks.ai AI Glossary","provider_url":"https://gaks.ai/glossary","title":"Generalization — AI Glossary","author_name":"Glenn Katrud Solheim","author_url":"https://gaks.ai","width":600,"height":200,"html":"<div style=\"font-family:sans-serif;border:1px solid #e0e0e0;border-radius:8px;padding:16px;max-width:600px;background:#ffffff;color:#111111;\"><p style=\"margin:0 0 4px;font-size:11px;color:#666;\">AI Glossary — gaks.ai</p><h3 style=\"margin:0 0 8px;font-size:16px;\">Generalization</h3><p style=\"margin:0 0 12px;font-size:14px;line-height:1.6;\">A model's ability to perform well on new, unseen data, not just the data it was trained on. Generalization is the central goal of machine learning, and the gap between training performance and real-world performance is what most evaluation and regularization methods try to close.  See also: overfitting, distribution shift, cross-validation.</p><a href=\"https://gaks.ai/glossary/generalization\" style=\"font-size:12px;color:#0077aa;\">Source: gaks.ai/glossary/generalization →</a></div>"}