A tree-structured model for decision-making, where each internal node tests an attribute and each leaf node represents an output (class or value). For example, a decision tree might split on “if EUR/USD > threshold” to decide on a buy vs sell action. Decision trees are easy to interpret and handle both classification and regression. In forex robots, simple decision trees can form rules (e.g. “if price is above moving average then buy”) or be components in more complex models like random forests. However, single trees can overfit small datasets unless pruned, so they’re often used with ensembling.

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