Random Forest is a supervised machine learning algorithm that creates a collection of decision trees to form a “forest.” It combines the predictions of multiple trees to improve accuracy and reduce the risk of overfitting, making it suitable for both classification and regression tasks.
Each tree is trained on a random subset of the data, and the final prediction is made by averaging or voting across all the individual trees. Random Forest is widely used in programming languages like R and Python for its effectiveness in handling large datasets and complex problems.