Overfitting vs Random Forest

Cryptocurrency trading model optimization to historical data that appears profitable in backtests but fails in live trading because parameters accidentally fit random noise rather than capturing genuine predictive patterns.

An ensemble machine learning algorithm that trains multiple decision trees independently and combines their predictions to improve accuracy and reduce overfitting in cryptocurrency trading models.