Technical Architecture and Implementation Principles of the Random Forest Algorithm
Random Forest is a powerful "Ensemble Learning" algorithm that achieves more stable and high-accuracy results by combining the predictions of numerous "Decision Tree" structures. By utilizing "Bagging" and "Feature Randomness" techniques, it minimizes the "overfitting" tendency of a single tree; thus, it is a "robust" model that exhibits high "generalization" success even with noisy data and does not require scaling.