#CurrencyPairPrediction Bagging (Bootstrap Aggregating) • Train many models on different random samples of the data. • Each model votes, and their average or majority decision is taken. Example: • 100 decision trees trained on different forex data samples. • Predict whether EUR/USD will go up or down. • Final decision = majority vote. Bagging Reduces: • Variance. • Overfitting. Popular Method: • Random Forests (a bagging ensemble of decision trees).