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EnsembleMethods
Bagging
Boosting
Stacking
Variance

What are ensemble methods, and how do they improve predictive performance compared to individual models? Explain the concept of model ensembling, including techniques like bagging, boosting, and stacking. Discuss the advantages of ensemble methods in terms of reducing variance, enhancing generalization, and handling complex relationships in data. Additionally, can you provide examples of real-world problems where ensemble methods have demonstrated significant improvements over single models, and what considerations should be taken into account when selecting and combining diverse models in an ensemble?

machine learning
Junior Level

Ensemble methods combine predictions from multiple individual models to improve overall predictive performance. They're founded on the principle that combining diverse models often leads to better results than using a single model. These methods effectively harness the...

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