Overfitting vs Supervised Learning

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.

A machine learning approach where algorithms learn patterns from labeled training data containing both inputs and correct outputs, enabling prediction of outputs from new unseen inputs after training completes.