A tree-structured model that makes decisions by splitting data on feature values. At each node, a rule (“if RSI > 50 then go right, else left”) partitions the data. Leaves of the tree give predictions. Decision trees are intuitive for forex: they mimic rule-based strategies (e.g. “if price above moving average, buy”). They handle both regression (predict price) and classification (predict up/down). Trees are popular because they are easy to interpret and visualize. However, single trees can overfit; ensembles (see Random Forest) often improve stability.

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