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 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.Open full answer →
58How does gradient boosting (XGBoost/LightGBM) work, and why does it dominate tabular ML?▼hard★ EssentialAmazonGoogleMeta1 replies◆ premiumXGBoost and LightGBM win most tabular problems, and interviewers want more than 'it's boosting.' The signal is the fit-to-residuals mechanism plus the engineering (regularization, histograms, second-order) that makes it both fast and accurate.Open full answer →
