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 →
38What are ensemble methods (bagging, boosting, stacking, blending), and why do ensembles work?▼mediumAmazonGoogleMeta1 replies◆ premiumEnsembles win Kaggle and quietly drive most production tabular models. What matters is explaining why combining models beats one (error decorrelation), and knowing exactly how the four techniques differ in what they fix.Open full answer →
61What is dropout, and how does it regularize a neural network?▼mediumGoogleMetaNVIDIA2 replies◆ premiumDropout is the classic neural-net regularizer, but what matters is whether you can explain why zeroing activations forces redundancy, and the train-vs-inference scaling bug that trips most candidates. Here is the answer.Open full answer →
