An ensemble method that builds multiple models on random subsets of the training data and averages their predictions. By combining many trees or classifiers trained on different samples, bagging reduces model variance and overfitting. In trading, bagging can be used (e.g. with random forests) to stabilize predictions from volatile forex data.

Back to Robots Glossary Index