{"version":"1.0","type":"rich","provider_name":"gaks.ai AI Glossary","provider_url":"https://gaks.ai/glossary","title":"Ensemble — 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;\">Ensemble</h3><p style=\"margin:0 0 12px;font-size:14px;line-height:1.6;\">A technique that combines the predictions of multiple models to produce a more accurate and robust result than any single model alone. Different models tend to make different mistakes, and aggregating their outputs reduces overall error. Ensembles are common in competition-winning machine learning pipelines and in applications where reliability matters more than speed.  See also: distillation, overfitting, baseline.</p><a href=\"https://gaks.ai/glossary/ensemble\" style=\"font-size:12px;color:#0077aa;\">Source: gaks.ai/glossary/ensemble →</a></div>"}