#CurrencyPairPrediction Build Multiple Decision Trees Random Forest builds hundreds or thousands of decision trees. Each tree is built differently: • Bootstrap Sampling: Randomly pick some samples (rows of data) with replacement (some rows can be picked multiple times). • Feature Randomness: At each split (node), only a random subset of features is considered, not all features. This randomness ensures that each tree is unique and catches different aspects of the data.