An ensemble learning algorithm that builds many decision trees and merges their outputs for final prediction. Each tree is trained on a random subset of data and (optionally) features, and their votes are aggregated (majority for classification, average for regression). This “forest” of trees reduces individual trees’ tendency to overfit and improves accuracy. Random forests work for both classification and regression tasks. In forex bots, random forests can aggregate signals from many decision rules to yield more stable trade predictions, often outperforming single trees on volatile currency data.

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