#CurrencyPairPrediction Boosting • Sequentially train models, each focusing on fixing the errors of the previous ones. • Later models “boost” the performance by correcting earlier mistakes. Example: • First model predicts EUR/USD return — makes 70% correct. • Second model focuses more on the 30% mistakes. • Repeat, improving at each step. Boosting Reduces: • Bias (underfitting). Popular Methods: • XGBoost • LightGBM • CatBoost Boosting is very powerful but can overfit if not regularized properly.