Ensemble learning is a machine learning paradigm that combines multiple models to improve overall performance and accuracy. By aggregating the predictions of various algorithms, ensemble methods can often outperform individual models, reducing errors and enhancing robustness. This technique leverages the strengths of diverse approaches, making it especially effective in scenarios where data may be noisy or complex.
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