What is the difference between Decision Trees and Random Forests?

A Decision Tree is a single model, while a Random Forest is an ensemble of multiple trees using bagging and feature randomness.

Random Forests improve Decision Trees by reducing variance through ensemble learning. Each tree is trained on a bootstrap sample and uses a random subset of features at each split. The final prediction is obtained by averaging (regression) or majority voting (classification). This decorrelation between trees significantly improves robustness and generalization.