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Random Forest is an ensemble learning technique that combines multiple individual models, usually decision trees, to create a more accurate and robust model. Each decision tree is trained on a different subset of the data and makes predictions independently. The final prediction is determined by aggregating the predictions of all the individual trees. Random Forest reduces overfitting and leads to higher accuracy compared to using a single decision tree. It also acts as a form of feature selection, filtering out noisy or irrelevant features.
