A random forest is a machine learning method that combines hundreds of decision trees, each trained on a random slice of the data, then averages their votes to make a prediction. Like polling a crowd of independent experts instead of trusting one, it is more accurate and less prone to overfitting than a single tree. Random forests excel on tabular business data: a Moroccan bank might use one to score credit risk from income, payment history, and employment data.