Random Forest

A random forest is an ensemble machine learning technique that builds multiple decision trees and merges their outputs to improve accuracy and reduce the risk of overfitting. This method excels in handling complex data sets for tasks like classification and regression, making it a go-to tool in predictive analytics where robustness against noisy data is crucial. In modern applications, it's valued for its ability to process vast amounts of variables without prior feature selection, powering everything from medical diagnoses to financial forecasting.