bagging

Ensembles dominate tabular ML, and this question checks whether you know they attack different parts of the error. The signal is bagging-reduces-variance vs boosting-reduces-bias and why GBMs win on tabular data. Here is the answer.

Ensembles win Kaggle and quietly power most production tabular models. The signal is explaining why combining models beats one (error decorrelation), and knowing exactly how the four techniques differ in what they fix.

Anyone can say 'ensemble of trees'. The real answer is about decorrelating those trees and why that, not just averaging, is what drops variance. Here is what makes the forest more than the sum of its trees.