bagging-reduces-variance-boosting-reduces-bias

IN premise — entries/2026/06/21/wiki-BiasE28093variance_tradeoff-chunk-1.md

Created 2026-06-21T09:55:49+00:00

Bagging (e.g., random forests) reduces variance; boosting (e.g., gradient boosting) reduces bias.

Dependents

These beliefs depend on this one:

• IN ensemble-methods-decompose-bias-variance-independently — Ensemble methods provide complementary and independent controls over the two components of prediction error: bagging (random forests) reduces variance by averaging decorrelated models, while boosting reduces bias by iteratively correcting residuals — together enabling targeted error reduction.