# Bagging

> Source: https://sukruyusufkaya.com/en/glossary/bagging
> Updated: 2026-05-13T20:00:17.438Z
> Type: glossary
> Category: makine-ogrenmesi
**TLDR:** An ensemble approach that improves stability by training multiple models on bootstrap samples and combining their outputs.

<p>Bagging is a powerful ensemble principle used to reduce model variance. The same algorithm is trained on different bootstrap samples, and predictions are then combined. This is especially effective for high-variance learners such as decision trees. Its main advantage is that it reduces the random sensitivity of a single model and yields more reliable predictions.</p>