Random Forest is a powerful and versatile supervised machine learning algorithm that excels in both classification and regression tasks. It operates by constructing a multitude of decision trees during training and outputs the mode of their predictions for classification or the mean prediction for regression. This ensemble method enhances predictive accuracy and mitigates the risk of overfitting, a common issue with individual decision trees. By leveraging the wisdom of the crowd, Random Forest combines the predictions of various trees, making it a robust choice for handling complex datasets and improving model performance across diverse applications.