Random Forest vs Boosting: Why Both Ensemble Methods Still Matter in ML

Random Forest and Boosting are both ensemble machine learning techniques, but they solve fundamentally different problems. Random Forest reduces variance by combining many independently trained decision trees, making predictions more stable and consistent. However, if all trees share the same blind spots, majority voting cannot correct those systematic errors. Boosting addresses this gap by training models sequentially, with each new model focusing specifically on the mistakes left by its predecessor. Understanding that one method targets stability while the other targets continuous error reduction explains why both remain essential tools in modern machine learning.