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Ensemble learning is a machine learning technique that combines multiple models to improve overall performance on tasks such as classification and regression. Key methods include bagging, boosting, and stacking, each with distinct approaches to model training and prediction aggregation. Bagging trains models independently on random data subsets, boosting trains models sequentially to learn from previous errors, and stacking uses a meta-learner to combine predictions from various models.

Author

raiarnav888

Language

EN