Random Forest vs Single Decision Tree

Medium

machine-learning Amazon Capital One Airbnb

Your single decision tree model shows high variance across different random seeds. You switch to a random forest of 100 trees and notice more stable predictions. What is the primary mechanism behind this improvement?

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

Correct!

Incorrect — try again next time!

Upgrade to Pro to unlock solutions and explanations

Random Forest vs Single Decision Tree

Multiple Choice

Related problems

Deep-dive guides