12Compare bagging and boosting, and random forests vs gradient boosting. When do you use each?▼medium★ EssentialAmazonGoogleMeta1 replies○ sign inEnsembles dominate tabular ML, and this question tests whether you grasp that 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.Open full answer →
101What actually makes a random forest work, beyond 'it averages a bunch of trees'?▼mediumAmazonMicrosoftGoogle2 replies◆ premiumAnyone can say 'ensemble of trees'. The real answer concerns decorrelating those trees and why that, not averaging alone, is what drops variance. Here is what makes the forest more than the sum of its trees.Open full answer →
