Random Forest

Random Forest is an ensemble learning technique used for classification and regression tasks that builds multiple decision trees and combines their predictions to make more accurate and robust predictions. It reduces overfitting and variance by aggregating the predictions of individual trees and improving generalization performance on unseen data. Readers can explore how random forest algorithms work, their advantages and limitations, and how to implement random forest models in machine learning frameworks like scikit-learn and XGBoost for classification and regression tasks, improving predictive modeling accuracy and robustness.