Random Forest vs Single Decision Tree

Medium

machine-learning Amazon Capital One Airbnb

Multiple Choice

Each tree is trained on the full dataset, so collectively they memorize all patterns Averaging predictions from decorrelated trees trained on bootstrap samples reduces variance The forest automatically prunes each tree to reduce overfitting Random forests use boosting to sequentially correct errors from previous trees

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