Random forest is an ensemble learning algorithm for classification and regression that operates by constructing a multitude of decision trees during training and outputting the aggregate prediction (majority vote for classification, average for regression)​.Each decision tree in the forest is trained on a random subset of the training data and typically using a random subset of features (a technique called feature bagging), which introduces diversity among the trees. The ensemble’s final prediction smooths out individual tree errors. This approach improves generalization: random forests handle many features well, are resistant to overfitting due to averaging, and can also provide estimates of feature importance​.They are widely used for their robustness and strong performance across various tasks, from image recognition to fraud detection​.