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Random Forest is a supervised machine learning algorithm that builds multiple decision trees and combines their outputs to improve accuracy and reduce overfitting. Each tree is trained on a random subset of data and features, which ensures diversity and prevents bias. For classification tasks, the algorithm uses majority voting, while for regression, it averages the predictions of all trees. Random Forest is widely used because it is robust, handles large datasets well, works with both categorical and numerical data, and provides high accuracy with minimal tuning. It is commonly applied in fields like finance, healthcare, and marketing for tasks such as fraud detection, disease prediction, and customer segmentation.