How Random Forest Fixes Decision Tree Instability Using Collective Predictions

Random Forest is a supervised machine learning algorithm that addresses the instability and overfitting problems of single Decision Trees by combining hundreds or thousands of them. Each tree is trained on a randomly sampled subset of the data — a technique called Bootstrap Sampling — ensuring every tree learns from a slightly different perspective. At each split, trees also consider only a random subset of features, reducing correlation between trees and forcing them to discover diverse patterns. For classification tasks, the final prediction is determined by majority vote across all trees, while regression tasks use the average of all predictions. This ensemble approach lowers variance, improves generalization, and produces more stable results, making Random Forest one of the most reliable general-purpose machine learning models.