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Random Forests is an ensemble learning method, combining multiple decision trees to enhance prediction accuracy and prevent overfitting. By aggregating the results of individual trees, it offers improved robustness and generalization capabilities for both classification and regression tasks in the context of Artificial Intelligence Algorithms and Machine Learning Methods. Its application within Technology and Artificial Intelligence focuses on delivering more precise and consistent outcomes by minimizing the impact of individual tree weaknesses or biases. The integration of multiple trees in Random Forests also enables better handling of complex data structures, facilitating advanced decision-making processes in various domains.