Random Forest vs Supervised Learning

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.

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.