Ensemble Learning

Thu, 09 Jul 2026 21:02:56 +1000

one model is an opinion; a committee is an estimator. 𐃏 ensemble methods build many imperfect predictors and combine them, and the two great families attack opposite ends of the bias-variance decomposition: bagging averages low-bias, high-variance models to cancel their wobble; boosting stacks up high-bias, low-variance weak learners to build accuracy that none of them has alone.

Boosting on Aayush Bajaj's Augmenting Infrastructure

Ensemble Learning