Random Forest is a powerful and versatile supervised machine learning algorithm that excels in both classification and regression tasks. It operates by constructing multiple decision trees during training and aggregating their predictions to improve accuracy and control overfitting. This ensemble method leverages the “wisdom of the crowd,” where the collective output of numerous trees leads to more reliable results than a single decision tree. Random Forest is particularly effective for handling large datasets and can manage missing values and outliers, making it a popular choice among data scientists for various applications in predictive modeling.